Complete Machine Learning & Data Science with Python | A-Z
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Hello there,
Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, my course on Udemy here to help you apply machine learning to your work.
Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course.
Do you know data science needs will create 11.5 million job openings by 2026?
Do you know the average salary is $100.000 for data science careers!
Data Science Careers Are Shaping The Future
Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.
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If you want to learn one of the employer’s most request skills?
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If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?
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If you are an experienced developer and looking for a landing in Data Science!
In all cases, you are at the right place!
We’ve designed for you “Complete Machine Learning & Data Science with Python | A-Z” a straightforward course for Python Programming Language and Machine Learning.
In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.
We will open the door of the Data Science and Machine Learning a-z world and will move deeper. You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn.
Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.
This Machine Learning course is for everyone!
My “Machine Learning with Hands-On Examples in Data Science” is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).
Why we use a Python programming language in Machine learning?
Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.
What you will learn?
In this course, we will start from the very beginning and go all the way to the end of “Machine Learning” with examples.
Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.
During the course you will learn the following topics:
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What is Machine Learning?
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More About Machine Learning
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Machine Learning Terminology
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Evaluation Metrics
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What is Classification vs Regression?
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Evaluating Performance-Classification Error Metrics
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Evaluating Performance-Regression Error Metrics
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Machine Learning with Python
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Supervised Learning
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Cross-Validation and Bias Variance Trade-Off
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Use Matplotlib and seaborn for data visualizations
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Machine Learning with SciKit Learn
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Linear Regression Theory
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Logistic Regression Theory
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Logistic Regression with Python
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K Nearest Neighbors Algorithm Theory
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K Nearest Neighbors Algorithm With Python
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K Nearest Neighbors Algorithm Project Overview
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K Nearest Neighbors Algorithm Project Solutions
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Decision Trees And Random Forest Algorithm Theory
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Decision Trees And Random Forest Algorithm With Python
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Decision Trees And Random Forest Algorithm Project Overview
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Decision Trees And Random Forest Algorithm Project Solutions
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Support Vector Machines Algorithm Theory
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Support Vector Machines Algorithm With Python
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Support Vector Machines Algorithm Project Overview
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Support Vector Machines Algorithm Project Solutions
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Unsupervised Learning Overview
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K Means Clustering Algorithm Theory
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K Means Clustering Algorithm With Python
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K Means Clustering Algorithm Project Overview
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K Means Clustering Algorithm Project Solutions
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Hierarchical Clustering Algorithm Theory
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Hierarchical Clustering Algorithm With Python
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Principal Component Analysis (PCA) Theory
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Principal Component Analysis (PCA) With Python
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Recommender System Algorithm Theory
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Recommender System Algorithm With Python
With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.
Why would you want to take this course?
Our answer is simple: The quality of teaching.
OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.
When you enroll, you will feel the OAK Academy`s seasoned developers’ expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.
Video and Audio Production Quality
All our videos are created/produced as high-quality video and audio to provide you the best learning experience.
You will be,
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Seeing clearly
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Hearing clearly
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Moving through the course without distractions
You’ll also get:
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Lifetime Access to The Course
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Fast & Friendly Support in the Q&A section
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Udemy Certificate of Completion Ready for Download
We offer full support, answering any questions.
If you are ready to learn the “Complete Machine Learning & Data Science with Python | A-Z” course.
Dive in now! See you in the course!
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1
What is Machine Learning?Machine learning is the science of getting computers to act without being explicitly programmed. In this lecture, we will find out What is machine learning? What are the terms used in machine learning?
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2
Machine Learning Terminology
Let's talk about the terminology of machine learning used in Machine Learning and which we will always encounter in next lessons
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3
Project Files -
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Quiz -
5
Quiz
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6
Classification vs Regression
In this lesson, we will introduce the Classification versus Regression
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7
Evaluating Performance: Classification Error Metrics
in this lesson, We will explain the performance classification of Classification Error Metrics.
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8
Evaluating Performance: Regression Error Metrics
In this lesson, We will learn the performance classification of Classification Error Metrics.
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9
Machine Learning With Python
In this lesson, we will discuss how to use Python and the “Scikit learn” package to perform machine learning with Python..
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10
Quiz
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12
Linear Regression Algorithm Theory
In this lesson, we will get a light theoretical background behind the idea of linear regression algorithm before actually tackling the concept with the Python and scikit learning library.
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13
Linear Regression Algorithm With Python Part 1
In this lesson, we will practice python for Linear Regression Algorithm.
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14
Linear Regression Algorithm With Python Part 2
In this lesson, we will continue to practice python for Linear Regression Algorithm.
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15
Linear Regression Algorithm Project Overview
In this lesson, we'll take a look at what to do with the Linear Regression Algorithm project.
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16
Linear Regression Algorithm Project Solutions
In this lesson, we will solutions the Linear Regression Algorithm Project
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18
Logistic Regression Algorithm Theory
In this lesson, we will discuss classification problems, how to use logistic regression algorithm to solve them, and how to interpret the results of logistic regression to read the confusion matrix.
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19
Logistic Regression Algorithm With Python Part 1
In this lesson, we will practice python for Logistic Regression Algorithm.
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20
Logistic Regression Algorithm With Python Part 2
In this lesson, we will continue to practice python for Logistic Regression Algorithm.
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21
Logistic Regression Algorithm With Python Part 3
In this lesson, we will continue to practice python for Logistic Regression Algorithm.
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22
Logistic Regression Algorithm Project Overview
In this lesson, we'll take a look at what to do with the Logistic Regression Algorithm project.
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23
Logistic Regression Algorithm Project Solutions
In this lesson, we will solutions the Logistic Regression Algorithm Project
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24
K Nearest Neighbors Algorithm Theory
In this lesson, we will get a light theoretical background behind the idea of K Nearest Neighbors Algorithm before actually tackling the concept with the Python.
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25
K Nearest Neighbors Algorithm With Python
In this lesson, we will practice python for K Nearest Neighbors Algorithm.
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26
K Nearest Neighbors Algorithm Project Overview
In this lesson, we'll take a look at what to do with the K Nearest Neighbors Algorithm project.
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27
K Nearest Neighbors Algorithm Project Solutions
In this lesson, we will solutions the K Nearest Neighbors Algorithm Project
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28
Decision Trees And Random Forest Algorithm Theory
In this lesson, we will discuss “decision trees” and “random forest” algorıthm as machine learning models.
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29
Decision Trees And Random Forest Algorithm With Python
In this lesson, we will show you how to use “scikit learn” in Python to create the “decision tree models” and “random forest models”.
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30
Decision Trees And Random Forest Algorithm Project Overview
In this lesson, we're going to give you some background about the project and go over the notebook for the project.
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31
Decision Trees And Random Forest Algorithm Project Solutions Part 1
In this lesson, we will go ahead and work through the code for the solutions.
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32
Decision Trees And Random Forest Algorithm Project Solutions Part 2
In this lesson, we will continue to practice python for “decision trees” and “random forest” algorıthm.
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33
Support Vector Machines Algorithm Theory
In this lesson, we will discuss the formal definition of “support vector machines”
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34
Support Vector Machines Algorithm With Python
In this lesson, we will use "scikit learn" in Python to implement "support vector machines" on some built in "breast cancer" data.
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35
Support Vector Machines Algorithm Project Overview
In this lesson we're just going to be doing an overview of the support vector machines project notebook.
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36
Support Vector Machines Algorithm Project Solutions
In this lesson, we will go over the solutions for the "support vector machines project".
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38
K Means Clustering Algorithm Theory
In this lesson. We will discuss the "K means clustering" algorithm, which will allow us to cluster unlabeled data and unsupervised machine learning algorithm.
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39
K Means Clustering Algorithm With Python
In this lesson, we will show you how to use "scikit learn" in Python to actually implement the " K means clustering ".
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40
K Means Clustering Algorithm Project Overview
In this lesson, we will get a brief overview of the "K stands for clustering" project.
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41
K Means Clustering Algorithm Project Solutions
In this lesson, we will begin to examine the solutions of the "K Means clustering" project.
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42
Hierarchical Clustering Algorithm Theory
In this lesson, we will learn about the theory behind “Hierarchical clustering” algorithm.
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43
Hierarchical Clustering Algorithm With Python
In this lesson, we will show you how to use “scipy” and “sklearn” in Python to create the “Hierarchical clustering models”.
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44
Principal Component Analysis (PCA) Theory
In this lesson, we're going to continue with the theory lesson a bit, and we'll start implementing Python and "scikit learn" commands.
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45
Principal Component Analysis (PCA) With Python
In this lesson, we will practice python for Principal Component Analysis (PCA).
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46
Recommender System Algorithm Theory
In this lesson, we will discuss the different types of recommender system and how to approach building a recommendation system with Python.
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47
Recommender System Algorithm With Python Part 1
In this lesson, we will show you how to create an item similarity recommender system for movies.
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48
Recommender System Algorithm With Python Part 2
In the lesson ofsecond part, we will actually create our recommendation system for movie data.
