Master Machine Learning and Data Science with Python
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- Curriculum
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Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?
In this comprehensive course, you’ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You’ll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. You’ll also learn text classification using Natural Language processing where you’ll deal with an interesting problem.
Data science has been recognized as one of the best jobs in the world and it’s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and that’s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.
I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.
I have 20 hours of best quality video contents. There are over 90 HD video lectures each ranging from 5 to 20 minutes on average. I’ve included Quizzes to test your knowledge after each topic to ensure you only leave the chapter after gaining the full knowledge. Not only that, I’ve given you many exercises to practice what you learn and solution to the exercise videos to compare the results. I’ve included all the exercise notebooks, solution notebooks, data files and any other information in the resource folder.
Now, I’m gonna answer the most important question. Why should you choose this course over the other courses?
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I cover all the important machine learning concepts in this course and beyond.
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When it comes to machine learning, learning theory is the key to understanding the concepts well. We’ve given the equal importance to the theory section which most of the other courses don’t.
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We’ve used the graphical tools and the best possible animations to explain the concepts which we believe to be a key factor that would make you enjoy the course.
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Most importantly, I’ve a dedicated section covering all the practical issues you’d face when solving machine learning problems. This is something that other courses tend to ignore.
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I’ve set the course price to the lowest possible amount so that anyone can afford the course.
Here a just a few of the topics we will be learning:
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Install Python and setup the virtual environment
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Learn the basics of Python programming including variables, lists, tuples, sets, dictionaries, if statements, for loop, while loop, construct a custom function, Python comprehensions, Python built-in functions, Lambda functions and dealing with external libraries.
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Use Python for Data Science and Machine Learning
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Learn in-dept theoretical aspects of all the machine learning models
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Open the data, perform pre-processing activities, build and evaluate the performance of the machine learning models Implement Machine Learning Algorithms
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Learn, Visualization techniques like Matplotlib and Seaborn
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Use SciKit-Learn for Machine Learning Tasks
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K-Means Clustering
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DBSCAN Clustering
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K-Nearest Neighbors
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Logistic Regression
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Linear Regression
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Lasso and Ridge – Regularization techniques
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Random Forest and Decision Trees and Extra Tree
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Naïve Bayes Classifier
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Support Vector Machines
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PCA – Principal Component Analysis
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Boosting Techniques – Adaboost, Gradient boost, XGBoost, Catboost and LightGBM
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Natural Language Processing
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How to deal with the practical problems when dealing with Machine learning
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6
Getting started with Python -
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Variables - Types
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Variables - Usage
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Variables - Strings
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Variables - Integers, Floats and Booleans
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Lists
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Tuples
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Dictionaries and Sets
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If Statements
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for loop
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while loop
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Custom Functions
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List Comprehensions
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Lambda Function
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Built-in Functions
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External Libraries
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Python Exercise Overview
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Python Exercise Solution - Part 1
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Python Exercise Solution - Part 2
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25
Introduction to Machine Learning
This gives a general broad idea of the machine learning and the different type of them.
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26
Introduction to Machine LearningThese quizzes are directly from Introduction to machine learning lecture video and you should be able to answer them comfortably.
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27
Machine Learning Life-Cycle
After learning the basic idea of what machine learning is, the best thing to learn next is to understand how the overall process happens in general. This is basically the life-cycle of a data science project presented as the machine learning life-cycle.
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Machine Learning Life-Cycle
Machine Learning Life-Cycle
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29
Introduction to Performance Evaluation - Classification
Since we are focusing on building models, we should be able to evaluate the performance of the model. This lecture helps to understand why the performance should be evaluated, how the data should be handle for evaluating the performance and how the performance of the classification problems are evaluated.
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30
Introduction to Performance Evaluation - Classification Metrics
Introduction to Performance Evaluation - Classification Metrics
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31
Confusion Matrix
This lecture explains the confusion matrix and it's related terminologies. It also explain the different types of errors and why we should focus on them individually.
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32
Confusion Matrix
These quizzes are directly from Confusion Matrix lecture video and you should be able to answer them comfortably.
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33
Main Classification Metrics
The Accuracy, Precision, Recall and F1 are the main classification metrics and they are briefly discussed here.
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34
Main Classification Metrics
These quizzes are directly from Main Classification Metrics lecture video and you should be able to answer them comfortably.
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35
Performance Evaluation - Regression
This topic is about the performance evaluation of regression problems which includes the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error.
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36
Performance Evaluation - Regression
These quizzes are directly from Performance Evaluation - Regression lecture video and you should be able to answer them comfortably.
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37
Introduction to Sklearn
This gives a basic understanding of what scikit-learn libraries are and how we use them.
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38
One Hot encoding
This gives a basic idea about the one hot encoding, what happens underneath when we perform the one hot encoding.
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Split the Data
We know that we should split the data because we need to evaluate the model's performance. But what happens when we split the data using the scikit-learn train test split library?
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What is Fit?
Whether building a model or performing a pre-processing activity, we'll be fitting the data all the time and I think we should know what exactly happens when we fit the data before actually do the fitting. This explains it well.
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Linear Regression Theory
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Linear Regression - Theory
These quizzes are directly from Linear Regression - Theory lecture video and you should be able to answer them comfortably.
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Linear Regression - Salary Prediction - Practical - Part 1
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Linear Regression - Salary Prediction - Practical - Part 2
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Linear Regression - House Price Prediction - Practical - Part 1
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Linear Regression - House Price Prediction - Practical - Part 2
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Linear Regression - Practical
These quizzes are directly from Linear Regression - Practical lecture video and you should be able to answer them comfortably.
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48
Logistic Regression - Theory
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Logistic Regression - Theory
These quizzes are directly from Logistic Regression - Theory lecture video and you should be able to answer them comfortably.
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Logistic Regression - Iris Flower - Practical
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51
Logistic Regression - Gender Classification - Exercise Overview
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Logistic Regression - Exercise Solution - Gender Classification - Part 1
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Logistic Regression - Exercise Solution - Gender Classification - Part 2
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54
Lasso and Ridge Regression - Theory
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Lasso and Ridge Regression - Theory
These quizzes are directly from Lasso and Ridge Regression - Theory lecture video and you should be able to answer them comfortably.
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Lasso and Ridge Regression - Melbourne Housing - Practice - Part 1
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Lasso and Ridge Regression - Melbourne Housing - Practice - Part 2
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Lasso and Ridge Regression - Melbourne Housing - Practice - Part 3
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Lasso and Ridge - Insurance - Exercise overview
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Lasso and Ridge - Insurance - Solution to the Exercise
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Bias Variance Trade-off
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Bias Variance Trade-off
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Dealing with Imbalanced Data
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Dealing with Imbalanced Data
Dealing with Imbalanced Data
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Dealing with Missing Values
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Dealing with Missing Values
Dealing with Missing Values
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Dealing with Outliers - Theory
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Dealing with Outliers - Practical
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Dealing with Outliers
Dealing with Outliers
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Feature Scaling of Data - Theory
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Feature Scaling - Practical
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Feature Scaling of Data
Feature Scaling of Data
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Random Forest - Theory
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Random Forest - Theory
Random Forest - Theory
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Random Forest - Practical - Bike Sharing - Part 1
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Random Forest - Practical - Bike Sharing - Part 2
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Random Forest - WeatherAUS - Exercise Overview
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Random Forest - weatherAUS - Solution Part 1
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Random Forest - weatherAUS - Solution Part 2
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Extra Tree - Theory
