Machine Learning & Deep Learning in Python & R
- Description
- Curriculum
- FAQ
- Reviews
You’re looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right?
You’ve found the right Machine Learning course!
After completing this course you will be able to:
· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy
· Answer Machine Learning, Deep Learning, R, Python related interview questions
· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.
Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. Here comes the importance of machine learning and deep learning. Knowledge on data analysis tools like R, Python play an important role in these fields of Machine Learning and Deep Learning.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. We have an in-depth knowledge on Machine Learning and Deep Learning techniques using data science and data analysis tools R, Python.
We are also the creators of some of the most popular online courses – with over 600,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. We aim at providing best quality training on data science, machine learning, deep learning using R and Python through this machine learning course.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on data science, machine learning, deep learning using R and Python. Each section contains a practice assignment for you to practically implement your learning on data science, machine learning, deep learning using R and Python.
Table of Contents
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Section 1 – Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Python basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.
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Section 2 – R basic
This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R. Similar to Python basics, R basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.
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Section 3 – Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation. This part of the course is instrumental in gaining knowledge data science, machine learning and deep learning in the later part of the course.
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Section 4 – Introduction to Machine Learning
In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
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Section 5 – Data Preprocessing
In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.
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Section 6 – Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
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Section 7 – Classification Models
This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don’t understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.
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Section 8 – Decision trees
In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R
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Section 9 – Ensemble technique
In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.
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Section 10 – Support Vector Machines
SVM’s are unique models and stand out in terms of their concept. In this section, we will discussion about support vector classifiers and support vector machines.
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Section 11 – ANN Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
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Section 12 – Creating ANN model in Python and R
In this part you will learn how to create ANN models in Python and R.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.
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Section 13 – CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.
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Section 14 – Creating CNN model in Python and R
In this part you will learn how to create CNN models in Python and R.
We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.
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Section 15 – End-to-End Image Recognition project in Python and R
In this section we build a complete image recognition project on colored images.
We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
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Section 16 – Pre-processing Time Series Data
In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models
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Section 17 – Time Series Forecasting
In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.
By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You’ll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.
Below is a list of popular FAQs of students who want to start their Machine learning journey-
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Why use Python for Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
Why use R for Machine Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
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IntroductionIn Lecture 1 of the Introduction section, we will begin by discussing the basics of machine learning and deep learning, outlining the differences between the two fields and exploring their applications in various industries. We will also delve into the history of machine learning and deep learning, tracing their evolution over the years and highlighting key milestones in their development.
Additionally, we will provide an overview of the tools and techniques that will be covered in this course, including Python and R programming languages. We will discuss the importance of these languages in the context of machine learning and deep learning, and explain how they can be effectively used to implement algorithms and models. By the end of this lecture, students will have a solid understanding of the foundational concepts in machine learning and deep learning, setting the stage for more advanced topics to come in future lectures. -
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Course Resources
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Installing Python and Anaconda
Join this section to learn how to install Python and get a crash course specifically tailored for data science. Whether you're new to Python or have prior experience, follow the step-by-step instructions to install Python from www.anaconda.com. Learn how to navigate the installation process and set up relevant libraries. Get ready to explore Microsoft VS Code as your coding environment. Start your data science journey with the right tools and foundational knowledge.
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This is a milestone!
Congratulations on embarking on this course and being part of the top 20 percent of enrolled students. Stay motivated and complete the course with the same energy. Your dedication inspires us to continually enhance the course and address your queries. Rate the course and provide feedback on teaching quality to help us improve.
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Opening Jupyter Notebook
Dive into the world of Jupyter Notebook with this tutorial. Learn three different methods to open Jupyter Notebook: through Anaconda Navigator, Anaconda Prompt, and Command Prompt. Follow step-by-step instructions to open Jupyter Notebook in your default browser and explore the home screen. Discover how to navigate through files and folders, change directories, and save files. Gain insights into the advantages of Jupyter Notebook over other tools and prepare for the upcoming lessons on Python syntax and Jupyter Notebook features.
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Introduction to Jupyter
Learn the basics of Jupyter Notebook in this tutorial. Discover how to create new cells and execute code in Python. Understand the concept of cells and their role in organizing code, comments, and outputs. Explore the different cell formats, including code, markdown, and raw. Learn essential shortcuts for editing cells, changing formats, and navigating between editable and non-editable modes. Gain a solid foundation in using Jupyter Notebook and prepare for the upcoming lessons on Python operations.
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Arithmetic operators in Python: Python Basics
In this tutorial, we will explore some of the basic arithmetic operations in Python using Jupyter Notebook. Learn how to perform addition, subtraction, multiplication, division, exponentiation, and modulus calculations. Observe the importance of using parentheses when dealing with multiple operators and how Python follows the BODMAS rule. Understand how to assign values to variables and perform arithmetic operations while defining variables. Additionally, discover Python's comparison operators, such as greater than, less than, equal to, greater than or equal to, and less than or equal to, and see how they return Boolean values based on the comparison results.
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Quick coding exercise on arithmetic operators -
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Strings in Python: Python Basics
In this tutorial, we delve into working with strings in Python. Learn how to define and assign strings using single or double quotes, and understand the importance of quotation marks. Explore string variables and how to display their values using direct execution or the print function. Discover string formatting using the format method and how to replace specific words within a string. Dive into string indexing and slicing, understanding how to extract individual characters or a range of characters from a string using index positions. Learn about the concept of index and position, as well as negative indexing. Explore the use of step parameters for skipping characters while slicing strings. Understand Python's dynamic typing, where variable types are inferred based on their assigned values, and learn how to determine the type of a variable using the type function.
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Quick coding exercise on String operations
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Lists, Tuples and Directories: Python Basics
This video provides an overview of lists, tuples, and dictionaries in Python. It explains the similarities and differences between them, how to define and manipulate them, and their use cases. The video also touches on conditional statements and loops. Gain insights into the fundamentals of these data structures and enhance your Python skills.
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Quick coding exercise on Tuples
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Working with Numpy Library of Python
Dive into the world of NumPy, a fundamental library in Python for numerical computations. Discover how to create and manipulate NumPy arrays, from one-dimensional to multi-dimensional arrays. Learn about initializing arrays, generating sequences, and even creating random matrices. Understand the importance of maintaining uniform data types in NumPy arrays and explore slicing operations for efficient data extraction. Get ready to unlock the full potential of NumPy for high-performance vector and matrix operations in Python.
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Quick coding exercise on NumPy Library
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Working with Pandas Library of Python
Discover the power of Pandas, a versatile software library for data manipulation and analysis in Python. Learn how to import data from CSV files and explore its structure using Pandas functions. Manipulate and transform data using indexing techniques and understand the significance of headers and indexes. Dive into descriptive statistics to gain insights into your data, and uncover the power of loc and iloc for efficient data extraction. With Pandas as your tool, unlock the potential for advanced data analysis and uncover valuable insights.
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Quick coding exercise on Pandas Library
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Working with Seaborn Library of Python
Step into the world of Seaborn, a powerful data visualization library for Python. Discover how Seaborn complements and enhances the plotting capabilities of Matplotlib. Learn to plot distributions, histograms, and scatter plots with customizable features. Dive into the Iris dataset and explore various visualizations, including scatter plots and pair plots, to uncover patterns and insights in the data. This Python course provides a glimpse into the potential of Seaborn and sets the stage for deeper explorations in data analysis and visualization.
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Python file for additional practice
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Quiz
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Installing R and R studio
In this lecture, we will focus on setting up R Studio and installing R on your computer. R Studio is a powerful integrated development environment (IDE) for R that provides a user-friendly interface for writing code, running commands, and visualizing data. We will walk through the installation process step by step, ensuring that you have the necessary software to follow along with the course material.
Additionally, we will provide a crash course in R, covering the basics of the programming language and how to perform common tasks such as data manipulation, visualization, and statistical analysis. By the end of this lecture, you will have a solid foundation in R and be ready to dive into more advanced topics in machine learning and deep learning using R Studio. -
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Basics of R and R studio
In Lecture 16 of the Machine Learning & Deep Learning in Python & R course, we will be covering the basics of R and R Studio. We will start by discussing how to set up R Studio on your computer, including downloading and installing the necessary software. We will then provide a crash course on using R, including an overview of basic programming concepts such as variables, functions, and data types.
Additionally, we will explore some key features of R Studio, such as the console, script editor, and environment pane. We will demonstrate how to write and execute code in R Studio, as well as how to load and manipulate data. By the end of this lecture, you will have a solid understanding of how to use R and R Studio for data analysis and machine learning tasks. -
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Packages in R
In Lecture 17 of Section 4 on "Setting up R Studio and R crash course," we will be covering the topic of packages in R. We will discuss the importance of packages in R for expanding the functionality of the language and how they can be used to streamline your data analysis and machine learning projects. We will explore how to install and load packages in R, as well as how to manage dependencies between different packages.
Additionally, we will delve into some popular packages in R that are commonly used in machine learning and deep learning projects. We will look at packages like caret, dplyr, ggplot2, and tidyr, and demonstrate how they can be used to clean and manipulate data, visualize data, build machine learning models, and perform other advanced data analysis tasks. By the end of this lecture, you will have a solid understanding of how to leverage packages in R to enhance your data analysis and machine learning workflows. -
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Inputting data part 1: Inbuilt datasets of R
In Lecture 18 of Section 4 of our Machine Learning & Deep Learning course, we will be focusing on inputting data into R Studio. We will start off by exploring the inbuilt datasets that R provides, which are great for practicing and learning data manipulation and analysis techniques. We will walk through how to access and load these datasets into R Studio, as well as how to explore and understand the structure of the data through summary statistics and visualizations. By the end of this lecture, you will have a foundational understanding of how to work with inbuilt datasets in R and be well-equipped to move on to more complex data analysis tasks.
Additionally, we will delve into a crash course on R, covering essential concepts and syntax that are crucial for data analysis. We will discuss basic data types in R, such as vectors, matrices, and data frames, as well as functions and loops that you can use to manipulate and analyze data. This crash course will provide you with the fundamental skills needed to effectively work with data in R Studio and set the stage for the more advanced topics we will cover in the rest of the course. Make sure to follow along and practice these concepts on your own to solidify your understanding and enhance your data analysis skills. -
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Inputting data part 2: Manual data entry
In this lecture, we will continue our discussion on inputting data into R Studio. We will focus on manual data entry techniques, including typing data directly into R Studio's console or script editor. We will cover the basics of entering data row by row, as well as inputting data in a tabular format. Additionally, we will discuss best practices for data entry, such as using proper formatting and labeling conventions to ensure that your data is accurately interpreted and analyzed.
Furthermore, we will explore the various functions and tools available in R Studio to facilitate manual data entry, such as the scan() function and the data entry toolbar. By the end of this lecture, you will have a solid understanding of how to input data manually in R Studio, allowing you to efficiently prepare your datasets for analysis and modeling using machine learning and deep learning techniques in R. -
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Inputting data part 3: Importing from CSV or Text files
In Lecture 20 of Section 4 of the course on Machine Learning & Deep Learning in Python & R, we will be focusing on importing data from CSV or text files into R Studio. We will discuss the process of reading and loading data from external sources using the read.csv function in R. We will explore the various parameters and options that can be set while importing data, such as specifying column names, data types, and how to handle missing values.
Additionally, we will cover the process of importing text files into R Studio using the read.table function. We will discuss the different options available for reading text files, such as setting delimiters, specifying column names, and handling headers and footers. By the end of this lecture, students will have a comprehensive understanding of how to efficiently import data from external sources into R Studio for analysis and manipulation in their machine learning and deep learning projects. -
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Creating Barplots in R
In this lecture, we will focus on setting up R Studio for data analysis and diving into a crash course on using R for machine learning tasks. We will cover the basics of installing R Studio, setting up the necessary packages, and navigating the user interface. Our goal is to familiarize ourselves with the R environment and understand its capabilities for data manipulation and analysis.
Additionally, we will explore the process of creating barplots in R for visualizing data. We will discuss the syntax and functions used to generate barplots, as well as how to customize them to suit our specific needs. By the end of this lecture, students will have a solid understanding of how to use R for data visualization and be able to apply these skills to their own machine learning projects. -
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Creating Histograms in R
In Lecture 22 of Section 4 of our Machine Learning & Deep Learning course, we will be diving into the topic of creating histograms in R. Histograms are a powerful visualization tool that allows us to see the distribution of our data in a clear and concise manner. We will start by discussing the importance of histograms in data analysis and how they can help us identify patterns and trends within our datasets.
Next, we will walk through the step-by-step process of creating histograms in R using R Studio. We will cover the different parameters and options available to customize our histograms, such as adding labels, changing colors, and adjusting bin sizes. By the end of this lecture, you will have a solid understanding of how to create histograms in R and be able to apply this knowledge to your own data analysis projects.
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Types of Data
In this video, we delve into the essentials that form the building blocks of statistical analysis. Learn about the crucial distinction between qualitative and quantitative data, exploring their subtypes: nominal and ordinal for qualitative data, and discrete and continuous for quantitative data. Discover how to identify and differentiate these data types, empowering you to choose the appropriate analysis techniques for your data. Prepare yourself with the key terminology discussed in this lecture for future statistical explorations.
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Types of Statistics
Dive into the realm of statistics and its two fundamental types: descriptive and inferential. In this video, we'll focus on descriptive statistics, which provide insights into data through measures of center and dispersion. Uncover the power of average, median, mode, range, and standard deviation in understanding data distributions. Dive into frequency distributions and bar charts for qualitative data and histograms for quantitative data. Additionally, explore inferential statistics, enabling us to draw conclusions and predictions from sample observations, with a special focus on neural networks for deep learning.
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Describing data Graphically
Join us in this informative video as we dive into the world of data summarization. Discover the power of frequency distributions, which allow us to organize qualitative and quantitative data into categories and determine the frequency of each category. Learn how to convert these distributions into visually appealing bar charts and construct histograms to visualize the distribution of data. Gain insights into the concepts of relative frequency and class width, and explore the properties of histograms. Lastly, we touch upon the important concept of normal distribution and its implications in data analysis.
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Measures of Centers
Join us in this insightful video as we unravel the world of descriptive numerical measures that help us understand the center of data. We delve into four essential measures: mean, median, mode, and mid-range. Learn how to calculate the mean, which represents the average value, and understand the distinction between population mean and sample mean. Discover the concept of median, the middle value in ordered data, and how to handle odd and even numbers of observations. Explore the mode, the value that occurs most frequently, and its relevance in qualitative data analysis. Lastly, grasp the concept of mid-range, which represents the average of the highest and lowest values. Gain valuable insights into when to use each measure based on data characteristics and outliers. Stay tuned for our next video, where we will dive into measures of dispersion.
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Measures of Dispersion
Dive into the world of measures of dispersion in this informative video lecture. Discover the three key measures: range, standard deviation, and variance. Learn how to calculate the range by finding the difference between the largest and smallest values, and understand its limitations when outliers are present. Explore the concepts of standard deviation and variance, their relationship, and their role in determining the spread of data. Gain insights into the significance of larger standard deviation values and their implications on data dispersion.
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Quiz
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Introduction to Machine Learning
Dive into the world of machine learning and discover its potential. Understand how machines learn from past data and improve their performance. Explore its applications in various industries, from banking to healthcare. Learn the distinction between machine learning, statistics, and artificial intelligence. Dive into supervised and unsupervised learning and their practical implementations. Gain insights into regression and classification problems and how they drive decision-making. Uncover the significance of data analysis and predictive modeling in shaping the future.
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Building a Machine Learning Model
This video outlines the seven crucial steps involved in building a machine learning model. Starting with problem formulation, it explains how to convert a business problem into a statistical problem. The subsequent steps cover data tidying and preprocessing, including data cleaning, filtering, missing value treatment, and outlier handling. The importance of splitting data into training and testing sets is emphasized, followed by training the model and assessing its performance. Finally, the video highlights the significance of using the prediction model, setting up a pipeline, monitoring outputs, and automating the scoring process.
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Gathering Business Knowledge
This video emphasizes the importance of understanding the business context when tackling a problem. It highlights the need to identify relevant variables and gather quality data for analysis, as the inputs greatly influence the outputs. The video discusses two approaches: primary research (interacting with stakeholders and experiencing the business firsthand) and secondary research (reviewing existing studies and reports). An example of cart abandonment in an online business is provided to illustrate the process.
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Data Exploration
This video emphasizes the three crucial steps of gathering relevant data for analysis. Firstly, it highlights the importance of identifying the required data based on business knowledge and research objectives. Secondly, it discusses the process of requesting data from internal and external sources, including teams within the organization and external data providers. Lastly, it emphasizes the significance of conducting a quality check on the received data. Using the example of cart abandonment, the video demonstrates how business understanding guides the selection of specific data elements to collect.
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The Dataset and the Data Dictionary
This video discusses the process of gathering and organizing data for analyzing house pricing in a real estate company. It highlights the importance of collating data from different sources and structuring it into a tabular format. The video emphasizes the need for a data dictionary that provides definitions of variables, including the dependent variable (price) and independent variables (e.g., crime rate, residential area proportion). It also mentions categorical variables, such as the presence of an airport or bus terminal.
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Importing Data in Python
In this tutorial, the presenter guides the audience on how to import house price data into Python using Jupyter Notebook. The step-by-step process includes launching Jupyter Notebook, setting up the working directory, and importing necessary libraries like NumPy, Pandas, and Seaborn for data analysis and visualization. The tutorial also demonstrates how to check the working directory and import the CSV file containing house price data. The final output shows the first five rows of the dataset and its dimensions (506 rows and 19 columns).
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Importing the dataset into R
In this lecture, we will focus on the essential step of data preprocessing in machine learning and deep learning. We will learn how to import datasets into the R programming language to effectively manipulate and analyze the data. By understanding the process of importing data, we can ensure that our datasets are clean, accurate, and ready for further analysis and modeling.
Throughout this lecture, we will explore various methods for importing datasets into R, including reading data from different file formats such as CSV, Excel, and text files. We will also cover techniques for handling missing data and outliers, as well as transforming data into the appropriate format for analysis. By the end of this lecture, students will have a solid foundation in importing datasets in R and be well-equipped to preprocess data for machine learning and deep learning applications. -
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Univariate analysis and EDD
In this tutorial, the presenter introduces univariate analysis, which focuses on analyzing individual variables in a dataset. The tutorial explains that descriptive statistics, such as mean, median, mode, range, quartiles, and standard deviations, are used to summarize and describe the data. For categorical variables, the count of each category is examined. The tutorial emphasizes the importance of Extended Data Dictionary (EDD) in identifying issues like outliers and missing values. The upcoming videos will delve into addressing these issues in data analysis.
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EDD in Python
This video focuses on conducting exploratory data analysis (EDA) and provides variable descriptions for a dataset. The EDA begins with calculating descriptive statistics using describe() function. The analysis includes examining mean, median, minimum, maximum, quartiles, and standard deviations for each variable. The script highlights the importance of EDD (Extended Data Dictionary) in identifying missing values, differences between mean and median, skewness, and potential outliers. Scatter plots are used to visualize relationships between variables. Categorical variables are analyzed using count plots. The video concludes with key observations regarding missing values, outliers, and certain variables with limited variation.
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EDD in R
In Lecture 37 of Section 7: Data Preprocessing, we will be diving into Exploratory Data Analysis (EDA) in R. EDA is a crucial step in the data preprocessing process that aims to uncover interesting patterns, relationships, and anomalies within the dataset. We will cover various techniques and tools in R that can be used to perform EDA, such as summary statistics, data visualization, and correlation analysis. By the end of this lecture, you will have a solid understanding of how to effectively explore and understand your dataset before diving into the modeling phase.
Additionally, we will discuss the importance of EDA in the context of machine learning and deep learning projects. Properly conducting EDA can help you identify potential issues in the data, select appropriate features for the model, and improve the overall performance of your predictive models. We will also cover best practices and common pitfalls to avoid when performing EDA in R. By the end of this lecture, you will be equipped with the necessary knowledge and skills to conduct effective EDA in R for your machine learning and deep learning projects. -
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Outlier Treatment
In this video, we explore the concept of outliers in data analysis and emphasizes the importance of addressing them before model training. Outliers, which deviate significantly from the overall pattern of a variable, can arise due to measurement or data entry errors. The script highlights the impact of outliers on mean, median, and standard deviation, emphasizing the need for careful handling to improve prediction accuracy. Various methods for identifying outliers, such as box plots, scatter plots, and histograms, are discussed. The script also presents approaches for treating outliers, including capping and floating, exponential smoothing, and the sigma approach.
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46
Outlier Treatment in Python
This video provides a step-by-step guide on how to identify and treat outliers in Python using the pandas and numpy libraries. It covers various techniques, such as capping and flooring, for handling outliers. The video demonstrates how to identify outliers by computing percentile values and then replace them with alternative values. Additionally, it explores the transformation of variables to remove outliers without eliminating them completely. With detailed explanations and code examples, this video offers a comprehensive approach to outlier treatment in Python.
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47
Outlier Treatment in R
In Lecture 40 of Section 7: Data Preprocessing in the course on Machine Learning & Deep Learning in Python & R, we will delve into the topic of outlier treatment in R. Outliers are data points that significantly deviate from the rest of the data and can have a substantial impact on the accuracy of machine learning models. We will discuss various techniques and methods for identifying and handling outliers in the data preprocessing stage, such as visualizing data distribution, using statistical tools like Z-score and IQR, and applying different transformation techniques.
Furthermore, we will explore the importance of outlier treatment in improving the overall performance and robustness of machine learning models. By effectively handling outliers, we can prevent biased results and enhance the reliability of the predictive models. This lecture will equip you with the necessary skills and knowledge to deal with outliers in your data sets, ensuring that your machine learning algorithms perform optimally and deliver accurate results. -
48
Missing Value Imputation
This video explores the common issue of missing values in datasets and discusses the challenges they pose in machine learning. It presents two options for handling missing values: removing the affected rows or replacing the missing values with neutral values. The video highlights various imputation techniques, such as imputing with zero, using central tendency measures (mean or median), assigning the most frequent category, or considering segment-specific means. The importance of utilizing business knowledge to select appropriate imputation methods is emphasized. Additionally, the video mentions that software tools can assist in identifying and filling missing values effectively.
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49
Missing Value Imputation in Python
This video focuses on handling missing values in Python. It discusses the identification of missing values using the info function and emphasizes the importance of treating missing values for accurate analysis. The video demonstrates the use of the fillna function to impute missing values, specifically using the mean of the n_hos_beds variable as an example. It highlights the option to perform missing value imputation for all columns using the fillna function with df.mean. The video concludes by emphasizing the need for tailored solutions based on specific variables and highlights the significance of saving the modified data frame.
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50
Missing Value imputation in R
In Lecture 43, we will delve into the important concept of missing value imputation in R. We will discuss the various techniques and methods used to handle missing data in a dataset, including mean imputation, median imputation, mode imputation, and machine learning algorithms for imputation. We will also explore the potential pitfalls and challenges associated with imputing missing values, as well as best practices for selecting the most appropriate imputation method based on the nature and distribution of the data.
Furthermore, we will explore the impact of missing data on machine learning models, and how improper handling of missing values can lead to biased results and inaccurate predictions. By the end of this lecture, students will have a comprehensive understanding of the importance of data preprocessing and missing value imputation in the context of machine learning and deep learning in R, and will be equipped with the knowledge and skills to effectively manage missing data in their own projects and analyses. -
51
Seasonality in Data
This video introduces the concept of seasonality in data, where recurring patterns occur over time. It explains how seasonality can be influenced by various factors such as weather or holidays, leading to fluctuations in data. To address the impact of seasonality on modeling, a correction factor is calculated. The video demonstrates an example of removing seasonality from sales data by multiplying the values with the calculated factors. The resulting normalized data is then suitable for analysis and model fitting.
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52
Bi-variate analysis and Variable transformation
In this informative video, we delve into the concept of bivariate analysis, which involves examining the relationships between two variables. We explore two popular methods: scatterplots and correlation matrices. By analyzing scatterplots, we determine whether there is a visible relationship between variables and assess if it's linear or nonlinear. Additionally, correlation matrices help us identify the strength and direction of correlations. We also discuss variable transformations, such as logarithmic and exponential functions, to achieve a more linear relationship. Join us as we apply these techniques to a dataset on house pricing and crime rates.
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53
Variable transformation and deletion in Python
In this video, we revisit our observations from EDD (Exploratory Data Analysis). We have already corrected missing values and outliers. Now, we focus on transforming the crime rate variable to establish a linear relationship with the price variable. By taking the logarithm of the crime rate variable, adding one to avoid undefined values, and plotting a joint plot, we observe a more linear relationship. We also create an average distance variable to represent the distances from the employment hub. Finally, we remove the redundant distance variables and the bus terminal variable from the dataset.
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54
Variable transformation in R
In Lecture 47 of Section 7 on Data Preprocessing, we will be diving into the topic of variable transformation in R. Variable transformation is an essential step in data preprocessing as it helps in improving the performance of machine learning models by making the data more suitable for analysis. This lecture will cover various techniques for variable transformation such as normalization, standardization, log transformation, and more.
We will discuss why variable transformation is needed, how to identify the need for transformation in the data, and the different methods that can be used based on the type of data and the requirements of the model. By the end of this lecture, you will have a clear understanding of how to preprocess and transform variables in R to enhance the performance of your machine learning and deep learning models. -
55
Non-usable variables
In this video, we dive into the process of selecting relevant variables for analysis. We explore four important points to determine the usefulness of variables. Firstly, variables with a single unique value, such as "bus terminal," are removed as they provide no meaningful information. Secondly, variables with a low fill rate and a large number of missing values are considered for deletion, as imputing values may not accurately capture their impact on the output. Thirdly, sensitive variables that may lead to discrimination or regulatory issues are excluded. Lastly, the importance of business knowledge and the use of bivariate analysis to validate logical relationships between variables are emphasized.
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56
Dummy variable creation: Handling qualitative data
This video focuses on the handling of categorical variables in regression analysis. It explains the need for creating dummy variables to represent non-numeric categories. The process involves assigning numerical values of 0 or 1 to each category, following a specific rule. The video clarifies the number of dummy variables required and emphasizes that nominal data cannot be assigned ordered numerical values. The interpretation of regression analysis results with dummy variables is discussed, which will be explored further in subsequent analysis.
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57
Dummy variable creation in Python
This video explains the process of creating dummy variables in Python to convert non-numerical categorical variables into numerical values. The example focuses on two variables: "airport" with categories "yes" and "no," and "waterbody" with categories "lake," "river," "lake and river," and "none." The video demonstrates how dummy variables are generated using the pandas function "get_dummies()" and showcases the resulting transformed data. It also discusses the need to delete redundant variables and emphasizes the importance of case sensitivity in Python programming.
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58
Dummy variable creation in R
In this lecture, we will be covering the topic of dummy variable creation in R as a part of the data preprocessing phase in machine learning and deep learning. Dummy variables are categorical variables that are converted into a numerical format to be used in statistical models. We will discuss why dummy variables are important and how they can be created in R using various methods such as one-hot encoding and label encoding.
Additionally, we will explore the concept of multicollinearity and how it can impact the performance of machine learning models when dealing with dummy variables. We will learn how to handle multicollinearity by dropping one of the dummy variables to avoid redundancy and ensure the accuracy of our models. By the end of this lecture, you will have a strong understanding of how to create dummy variables in R and how to effectively preprocess your data for machine learning and deep learning applications. -
59
Correlation Analysis
This video explores the concept of correlation and correlation coefficient in data analysis. It explains how correlation helps identify the relationship between variables and classifies them as positively or negatively correlated. The correlation coefficient, ranging from -1 to 1, quantifies the correlation strength. However, it emphasizes that correlation does not imply causation and discusses the importance of distinguishing between the two. The video demonstrates the use of a correlation matrix to analyze multiple variables' correlations and highlights how identifying highly correlated independent variables can help avoid multicollinearity in statistical models.
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60
Correlation Analysis in Python
This video explores the use of correlation metrics in data analysis. It explains how correlation matrices provide insights into the relationships between variables. By examining correlation coefficients, variables with high correlations to the dependent variable can be identified as important for analysis. The video also highlights the issue of multicollinearity caused by high correlations between independent variables and suggests methods for selecting variables to mitigate this problem. Ultimately, the video demonstrates how correlation metrics help determine variable importance and support data-driven decision-making.
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61
Correlation Matrix in R
In this lecture on data preprocessing, we will delve into the concept of correlation matrix in R. A correlation matrix is a table that displays the correlation coefficients between many variables. It is a crucial tool in data analysis as it helps us understand the relationship between different variables in a dataset. We will learn how to create a correlation matrix in R using various functions and packages, and how to interpret the results obtained from the correlation matrix.
Additionally, we will explore the significance of correlation analysis in machine learning and deep learning models. Understanding the correlation between variables can help us identify redundant or irrelevant features in our dataset, which can improve the performance of our models. We will discuss how to use the information from the correlation matrix to select the most relevant features for our machine learning algorithms, and how to preprocess the data accordingly to optimize the model's performance. -
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Quiz
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63
The Problem Statement
This video delves into the fundamentals of linear regression, a simple yet powerful approach for supervised learning. It emphasizes the significance of understanding linear regression before exploring more complex machine learning methods. The video introduces key concepts and focuses on the widely used least square approach for fitting linear models. Using a house pricing dataset as an example, it showcases how linear regression can accurately predict house prices and estimate the impact of individual variables. This video serves as a comprehensive guide to mastering linear regression for effective predictive modeling.
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64
Basic Equations and Ordinary Least Squares (OLS) method
In this video, we explore the concept of simple linear regression as a straightforward approach for predicting house prices based on a single predictor variable. By assuming a linear relationship between the predictor variable (number of rooms) and the target variable (house price), we formulate a mathematical equation to estimate the coefficients of the linear model. The video introduces the least square method as a measure of model fit and explains how to minimize the sum of squared residuals to obtain the optimal coefficients. The video emphasizes the importance of understanding the concepts behind linear regression and interpreting the results rather than memorizing the mathematical formulas.
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65
Assessing accuracy of predicted coefficients
In this video, we explore the accuracy of regression coefficients beta 0 and Beta 1 in predicting house prices based on the number of rooms. We analyze a small sample of 506 observations and discuss the difference between the sample regression line and the population regression line. By calculating the standard error, we establish confidence intervals for the population coefficients. Additionally, we introduce hypothesis testing using the t statistic and P-value to determine the significance of the relationship between the predictor and response variables.
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66
Assessing Model Accuracy: RSE and R squared
In this video, we evaluate the accuracy of our created model by looking at two key measures: the residual standard error (RSE) and the coefficient of determination (R-squared). The RSE, calculated as the average deviation of the response from the regression line, provides insights into the model's lack of fit. On the other hand, R-squared measures the proportion of variability in the response variable explained by the model. An R-squared value close to 1 indicates a strong fit, while values around 0 indicate a poor fit. Generally, an R-squared greater than 0.5 is considered good for most applications.
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67
Simple Linear Regression in Python
In this tutorial, we explore how to build a simple linear regression model in Python using two different libraries: statsmodels.api and sklearn. We import the necessary libraries and define our dependent variable (Y) and independent variable (X). We fit the model, analyze the summary statistics including coefficients, p-values, and R-squared, and interpret the results. Additionally, we demonstrate how to predict values, plot the regression line, and provide insights into interpreting the slope and intercept of the line.
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68
Simple Linear Regression in R
In this lecture, we will delve into the concept of Simple Linear Regression using R. We will start by understanding the basics of linear regression, its applications, and how it is used in machine learning and deep learning models. We will then explore how to implement simple linear regression in R, including how to prepare the data, fit the model, and make predictions using real-world datasets.
Next, we will discuss the assumptions of linear regression, such as linearity, independence, homoscedasticity, and normality. We will also cover how to evaluate the model's performance using metrics like R-squared, adjusted R-squared, and the standard error of the estimate. By the end of this lecture, students will have a solid understanding of Simple Linear Regression in R and be able to apply this knowledge to their own machine learning and deep learning projects. -
69
Multiple Linear Regression
In this tutorial, we delve into the concept of multiple linear regression, which allows us to analyze the relationship between a dependent variable and multiple predictor variables. We extend our analysis from simple linear regression to accommodate the complexity of real-world scenarios with numerous predictors. We discuss the mathematical formulation of the multiple regression equation and interpret the coefficients, which quantify the impact of each predictor on the dependent variable while holding other variables constant. We explore the significance of the coefficients through standard errors, t-values, and p-values. The tutorial also presents the results of a multiple linear regression model run on a housing dataset, highlighting the multiple R-squared and adjusted R-squared values that indicate the model's ability to explain the variance in the dependent variable.
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70
The F - statistic
In this informative video, we explore the significance of the F-statistic in multiple linear regression. While individual t-values and p-values can suggest relationships between predictors and the response variable, they can lead to incorrect conclusions, especially with a large number of variables. We introduce the F-statistic, which considers the number of predictors, and discuss its role in determining the overall significance of the model. By comparing the F-statistic's p-value against a threshold, we can ensure that the chosen predictors have a significant impact on the response variable.
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71
Interpreting results of Categorical variables
In this video, we dive into the results obtained for the categorical variables in our dataset. Two categorical variables, "airport" and "water body," were converted into corresponding dummy variables to fit the linear regression model. We interpret the coefficients and p-values to understand the impact of these variables on the house prices. The "airport" variable suggests a significant increase in house prices when an airport is present. However, the "water body" variables (lake and river) show no statistical evidence of influencing house prices.
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72
Multiple Linear Regression in Python
In this tutorial, we explore the process of constructing multiple linear regression models in Python. We begin by utilizing the statsmodels and sklearn libraries to create our models. Through code examples, we demonstrate how to prepare the independent and dependent variables, add a constant term, fit the model, and obtain model summaries. The significance of variables and their coefficients are discussed, emphasizing the interpretation of signs and p-values. Additionally, we compare the results obtained from statsmodels and sklearn, highlighting their respective advantages.
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73
Multiple Linear Regression in R
In this lecture, we will delve into the topic of Multiple Linear Regression in R. We will start by understanding the basic concept of Multiple Linear Regression, which involves predicting a continuous dependent variable based on two or more independent variables. We will discuss how to create a regression model using multiple features and how to interpret the coefficients of the model.
Next, we will learn how to implement Multiple Linear Regression in R. We will cover how to preprocess the data, split it into training and testing sets, and then train the model using the lm() function in R. We will also discuss how to interpret the results of the regression analysis, including assessing the model's accuracy and significance of the predictors. By the end of this lecture, students will have a solid understanding of Multiple Linear Regression in R and be able to apply this knowledge to real-world datasets. -
74
Test-train split
The script discusses the significance of test data in assessing the accuracy of predictive models. It emphasizes the difference between training and test errors and the need to avoid overfitting. The three popular techniques for dividing data into training and test sets—validation set, leave one out cross-validation, and K-fold cross-validation—are explained. The goal is to find the model with the best test error, ensuring better predictions for unseen data.
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75
Bias Variance trade-off
This video delves into the concept of the bias-variance tradeoff, a fundamental aspect of model evaluation. It explains how the expected test error is influenced by variance and bias, while acknowledging the presence of an irreducible error. Variance represents the sensitivity of a model's predictions to changes in the training data, while bias captures the error introduced by oversimplifying a complex relationship. The script illustrates how increasing model flexibility leads to higher variance and lower bias, emphasizing the need to strike a balance to minimize overall error. The tradeoff is visually depicted, highlighting the search for the optimal point where bias and variance are minimized.
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76
Test train split in Python
This video demonstrates how to split data into training and test sets using the test train split function in Python's scikit-learn library. It explains the process of importing the function, defining the independent and dependent variables, specifying the test size ratio, and setting a random state for reproducibility. The video shows how to create a linear regression model, train it using the training set, and make predictions on the test set. Finally, it calculates the R-square values for both the training and test data to evaluate the model's performance.
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77
Test-Train Split in R
In this lecture, we will delve into the intricacies of test-train split in R when applying linear regression models. We will discuss the importance of splitting our dataset into a training set and a test set to evaluate the performance of our model. We will explore how to implement the test-train split using various methods available in R, such as the `createDataPartition` function from the `caret` package.
Furthermore, we will walk through the process of assessing the performance of our linear regression model using the test set. We will cover key metrics such as the root mean squared error (RMSE) and the coefficient of determination (R-squared) to evaluate the model's accuracy and effectiveness. By the end of this lecture, you will have a solid understanding of how to effectively split your dataset for training and testing purposes in R when working with linear regression models. -
78
Regression models other than OLS
This video introduces alternative linear models that provide improved prediction accuracy and model interpretability compared to the standard linear model. It highlights the limitations of ordinary least squares (OLS) regression, especially when dealing with a large number of variables or a small number of observations. The video discusses the concept of variable selection to exclude irrelevant variables and focuses on two types of methods: subset selection, where a subset of variables is used in the model, and shrinkage methods (regularization), which shrink coefficients towards zero. These alternative models aim to enhance both accuracy and interpretability.
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79
Subset selection techniques
In this video, different types of subset selection techniques for linear models are discussed. The three main methods covered are best subset selection, forward stepwise selection, and backward stepwise selection. Best subset selection involves fitting separate regression models for each combination of predictor variables and selecting the best model based on R-squared. Forward stepwise selection starts with no predictors and gradually adds one variable at a time until all predictors are included. Backward stepwise selection begins with all predictors and removes them one by one. These techniques provide computationally efficient alternatives to best subset selection, although they may not guarantee the best model.
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80
Subset selection in R
In Lecture 72 of Section 8 on Linear Regression, we will be diving into subset selection in R. Subset selection is a technique used to select a subset of predictor variables that most significantly contribute to predicting the outcome variable in a regression model. This process helps to simplify the model by eliminating irrelevant variables, improving the model's accuracy and interpretability. In this lecture, we will learn different methods of subset selection, such as forward selection, backward elimination, and stepwise regression, and how to implement them in R.
We will also discuss the advantages and disadvantages of each subset selection method, as well as the importance of selecting the right subset of variables based on the specific problem at hand. By the end of this lecture, you will have a better understanding of how subset selection can improve the performance of your linear regression models and how to effectively apply these techniques in R to build more robust and accurate predictive models. -
81
Shrinkage methods: Ridge and Lasso
In this video, we explore shrinkage methods for linear regression models. The two main techniques discussed are Ridge regression and Lasso. Ridge regression involves minimizing a modified quantity that includes a shrinkage penalty, which helps reduce the coefficient values towards zero. This technique improves model bias and variance trade-off and requires standardized predictor variables. On the other hand, Lasso performs variable selection by allowing some coefficients to become exactly zero. It leads to a more interpretable model compared to Ridge regression. The choice between the two methods depends on the number of predictor variables and the expected relationship with the response variable.
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82
Ridge regression and Lasso in Python
Learn how to run ridge and lasso regression in Python in this lecture. Start by standardizing the data using the preprocessing module from sklearn. Then, create the Ridge object and fit the model. Find the best lambda value by using the validation curve and select the model with the highest R-squared value. Finally, fit the ridge model with the best lambda on the training dataset and calculate the R-squared values for both the training and test datasets.
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83
Ridge regression and Lasso in R
In Lecture 75 of Section 8: Linear Regression, we will be diving into the topics of Ridge regression and Lasso in R. These are two popular regularization techniques used to prevent overfitting in machine learning models. We will explore how Ridge regression adds a penalty term to the cost function based on the squared magnitude of the coefficients, while Lasso adds a penalty term based on the absolute magnitude of the coefficients. We will discuss how these techniques help to shrink the coefficients towards zero, ultimately improving the model's generalization ability.
Additionally, we will cover the implementation of Ridge regression and Lasso in R using the appropriate packages and functions. We will walk through examples of how to fit Ridge and Lasso models to data, tune the hyperparameters, and evaluate the model performance. By the end of this lecture, students will have a solid understanding of how to apply these regularization techniques in their machine learning projects using R. -
84
Heteroscedasticity
This video addresses the issue of heteroskedasticity in models. Heteroskedasticity refers to the non-constant variance of error terms, often increasing with the response variable's values. Graphically, this is observed as a funnel-like shape when plotting residuals against the response variable. To handle heteroskedasticity, scaling down larger response values is recommended, using methods like taking the logarithm or square root. By doing so, the residuals tend to exhibit constant variance. This video explains how to identify and address heteroskedasticity in a model.
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85
Three classification models and Data set
In this section on classification models, we explore logistic regression, K-nearest neighbors (KNN), and Linear Discriminant Analysis (LDA). Classification problems involve predicting categorical variables, such as determining if a football player will score a goal or if a patient has a heart issue. These techniques provide accurate predictions without being computationally heavy. The training data focuses on predicting the selling potential of properties based on past transactions, classifying them as sold (1) or not sold (0) within three months. The data is preprocessed, and the upcoming videos will cover importing the dataset and implementing the models.
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86
Importing the data into Python
In this script, we learn how to import a CSV file using the pandas library in Python. The script demonstrates the use of the pd.read_csv function to read the CSV file into a dataframe. The location of the CSV file is specified as the first argument, and the presence of headers is indicated by setting header=0. The script also mentions converting backslashes to forward slashes in the file location if using Windows.
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87
Importing the data into R
In this lecture, we will delve into the basics of classification models in machine learning and deep learning. We will understand the significance of classifying data into categories and the different types of classification models that can be used to achieve this task. We will also explore the fundamental concepts behind classification models and their applications in various fields such as finance, healthcare, and marketing.
Furthermore, we will learn how to import data into R, a powerful tool for statistical computing and data analysis. Importing data is a crucial step in the data analysis process, as it allows us to manipulate and analyze the data to make informed decisions. We will cover different methods of importing data into R, such as reading data from files, accessing data from databases, and connecting to external sources. By the end of this lecture, students will have a solid understanding of classification models and how to import data into R for further analysis and modeling. -
88
The problem statements
This video introduces two types of business questions that can be answered using the model being built: prediction questions and inferential questions. Prediction questions focus on accurately predicting whether a house will be sold within three months of being listed, without considering the individual variables' impact. Inferential questions, on the other hand, aim to determine the importance and impact of each independent variable on the response variable. The video mentions using various classifiers to find answers to these questions.
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89
Why can't we use Linear Regression?
This video highlights the limitations of using linear regression for classification tasks and introduces the concept of logistic regression. Using an example dataset, the video explains that linear regression cannot handle response variables with more than two levels and does not provide probability values. It also discusses the sensitivity of linear regression to outliers, leading to incorrect predictions. The upcoming discussion will delve into logistic regression and how it addresses these issues.
