Machine Learning A-Z- AI, Python R + ChatGPT Prize [2024]

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024], available at $139.99, has an average rating of 4.51, with 472 lectures, 37 quizzes, based on 189063 reviews, and has 1079300 subscribers.

You will learn about Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem This course is ideal for individuals who are Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning tools. It is particularly useful for Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning tools.

Enroll now: Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Summary

Title: Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Price: $139.99

Average Rating: 4.51

Number of Lectures: 472

Number of Quizzes: 37

Number of Published Lectures: 386

Number of Published Quizzes: 33

Number of Curriculum Items: 509

Number of Published Curriculum Objects: 419

Number of Practice Tests: 2

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Master Machine Learning on Python & R

Have a great intuition of many Machine Learning models

Make accurate predictions

Make powerful analysis

Make robust Machine Learning models

Create strong added value to your business

Use Machine Learning for personal purpose

Handle specific topics like Reinforcement Learning, NLP and Deep Learning

Handle advanced techniques like Dimensionality Reduction

Know which Machine Learning model to choose for each type of problem

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Who Should Attend

Anyone interested in Machine Learning.

Students who have at least high school knowledge in math and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Machine Learning tools.

Target Audiences

Anyone interested in Machine Learning.

Students who have at least high school knowledge in math and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Machine Learning tools.

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

Over 1 Million students world-wide trust this course.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

Part 1 – Data Preprocessing

Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 – Clustering: K-Means, Hierarchical Clustering

Part 5 – Association Rule Learning: Apriori, Eclat

Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And last but not least, this course includes both Python and R code templateswhich you can download and use on your own projects.

Course Curriculum

Chapter 1: Welcome to the course! Here we will help you get started in the best conditions.

Lecture 1: Welcome Challenge!

Lecture 2: Machine Learning Demo – Get Excited!

Lecture 3: Get all the Datasets, Codes and Slides here

Lecture 4: How to use the ML A-Z folder & Google Colab

Lecture 5: Installing R and R Studio (Mac, Linux & Windows)

Lecture 6: EXTRA: Use ChatGPT to Boost your ML Skills

Chapter 2: – Part 1: Data Preprocessing –

Lecture 1: Welcome to Part 1 – Data Preprocessing

Lecture 2: The Machine Learning process

Lecture 3: Splitting the data into a Training and Test set

Lecture 4: Feature Scaling

Chapter 3: Data Preprocessing in Python

Lecture 1: Getting Started – Step 1

Lecture 2: Getting Started – Step 2

Lecture 3: Importing the Libraries

Lecture 4: Importing the Dataset – Step 1

Lecture 5: Importing the Dataset – Step 2

Lecture 6: Importing the Dataset – Step 3

Lecture 7: For Python learners, summary of Object-oriented programming: classes & objects

Lecture 8: Taking care of Missing Data – Step 1

Lecture 9: Taking care of Missing Data – Step 2

Lecture 10: Encoding Categorical Data – Step 1

Lecture 11: Encoding Categorical Data – Step 2

Lecture 12: Encoding Categorical Data – Step 3

Lecture 13: Splitting the dataset into the Training set and Test set – Step 1

Lecture 14: Splitting the dataset into the Training set and Test set – Step 2

Lecture 15: Splitting the dataset into the Training set and Test set – Step 3

Lecture 16: Feature Scaling – Step 1

Lecture 17: Feature Scaling – Step 2

Lecture 18: Feature Scaling – Step 3

Lecture 19: Feature Scaling – Step 4

Chapter 4: Data Preprocessing in R

Lecture 1: Getting Started

Lecture 2: Dataset Description

Lecture 3: Importing the Dataset

Lecture 4: Taking care of Missing Data

Lecture 5: Encoding Categorical Data

Lecture 6: Splitting the dataset into the Training set and Test set – Step 1

Lecture 7: Splitting the dataset into the Training set and Test set – Step 2

Lecture 8: Feature Scaling – Step 1

Lecture 9: Feature Scaling – Step 2

Lecture 10: Data Preprocessing Template

Chapter 5: – Part 2: Regression –

Lecture 1: Welcome to Part 2 – Regression

Chapter 6: Simple Linear Regression

Lecture 1: Simple Linear Regression Intuition

Lecture 2: Ordinary Least Squares

Lecture 3: Simple Linear Regression in Python – Step 1a

Lecture 4: Simple Linear Regression in Python – Step 1b

Lecture 5: Simple Linear Regression in Python – Step 2a

Lecture 6: Simple Linear Regression in Python – Step 2b

Lecture 7: Simple Linear Regression in Python – Step 3

Lecture 8: Simple Linear Regression in Python – Step 4a

Lecture 9: Simple Linear Regression in Python – Step 4b

Lecture 10: Simple Linear Regression in Python – Additional Lecture

Lecture 11: Simple Linear Regression in R – Step 1

Lecture 12: Simple Linear Regression in R – Step 2

Lecture 13: Simple Linear Regression in R – Step 3

Lecture 14: Simple Linear Regression in R – Step 4a

Lecture 15: Simple Linear Regression in R – Step 4b

Lecture 16: Simple Linear Regression in R – Step 4c

Chapter 7: Multiple Linear Regression

Lecture 1: Dataset + Business Problem Description

Lecture 2: Multiple Linear Regression Intuition

Lecture 3: Assumptions of Linear Regression

Lecture 4: Multiple Linear Regression Intuition – Step 3

Lecture 5: Multiple Linear Regression Intuition – Step 4

Lecture 6: Understanding the P-Value

Lecture 7: Multiple Linear Regression Intuition – Step 5

Lecture 8: Multiple Linear Regression in Python – Step 1a

Lecture 9: Multiple Linear Regression in Python – Step 1b

Lecture 10: Multiple Linear Regression in Python – Step 2a

Lecture 11: Multiple Linear Regression in Python – Step 2b

Lecture 12: Multiple Linear Regression in Python – Step 3a

Lecture 13: Multiple Linear Regression in Python – Step 3b

Lecture 14: Multiple Linear Regression in Python – Step 4a

Lecture 15: Multiple Linear Regression in Python – Step 4b

Lecture 16: Multiple Linear Regression in Python – Backward Elimination

Lecture 17: Multiple Linear Regression in Python – EXTRA CONTENT

Lecture 18: Multiple Linear Regression in R – Step 1a

Lecture 19: Multiple Linear Regression in R – Step 1b

Lecture 20: Multiple Linear Regression in R – Step 2a

Lecture 21: Multiple Linear Regression in R – Step 2b

Lecture 22: Multiple Linear Regression in R – Step 3

Lecture 23: Multiple Linear Regression in R – Backward Elimination – HOMEWORK !

Lecture 24: Multiple Linear Regression in R – Backward Elimination – Homework Solution

Lecture 25: Multiple Linear Regression in R – Automatic Backward Elimination

Chapter 8: Polynomial Regression

Lecture 1: Polynomial Regression Intuition

Lecture 2: Polynomial Regression in Python – Step 1a

Lecture 3: Polynomial Regression in Python – Step 1b

Instructors

Kirill Eremenko
DS & AI Instructor

Hadelin de Ponteves
Passionate AI Instructor

SuperDataScience Team
Helping Data Scientists Succeed

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 1842 votes

2 stars: 2912 votes

3 stars: 16827 votes

4 stars: 63929 votes

5 stars: 103554 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!