Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2025]

$0.99
Instructor:
Kirill Eremenko, Hadelin de Ponteves
Category:

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.

What you’ll 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
Course content
36

46 sections • 386 lectures • 42h 44m total length

Welcome to the course! Here we will help you get started in the best conditions.
Data Preprocessing in Python
Data Preprocessing in R
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
Regression Model Selection in Python
Regression Model Selection in R
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Classification Model Selection in Python
Evaluating Classification Models Performance
K-Means Clustering
Hierarchical Clustering
Apriori
Eclat
Upper Confidence Bound (UCB)
Thompson Sampling
Artificial Neural Networks
Convolutional Neural Networks
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
Model Selection
XGBoost
Annex: Logistic Regression (Long Explanation)
Congratulations!! Don't forget your Prize :)