Machine Learning A-Z™: Hands-On Python & R In Data Science – Course by Kirill Eremenko, Hadelin de Ponteves

A comprehensive course to teach you Machine Learning on Python & R, accurate predictions, powerful analyses, robust Machine Learning models; how to handle Reinforcement Learning, NLP, Deep Learning, Dimensionality Reduction etc.

This course has been designed by two professional Data Scientists who 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. The ten parts of this course go from Data Preprocessing to Classification, Clustering, Deep Learning etc.

Moreover, the course is packed with practical exercises which are based on live examples, giving you hands-on practice building your own models.

And as a bonus, you get both Python and R code templates which you can download and use on your own projects; 19 articles; full lifetime access; certificate of completion.

Welcome to the course!

• Applications of Machine Learning
• Why Machine Learning is the Future
• Installing R and R Studio (MAC & Windows)
• Update: Recommended Anaconda Version
• Installing Python and Anaconda (MAC & Windows)

Part 1: Data Preprocessing

• Welcome to Part 1 – Data Preprocessing
• Get the dataset
• Importing the Libraries
• Importing the Dataset
• For Python learners, summary of Object-oriented programming: classes & objects
• Missing Data
• Categorical Data
• Splitting the Dataset into the Training set and Test set
• Feature Scaling
• And here is our Data Preprocessing Template!
• Data Preprocessing

Part 2: Regression

• Welcome to Part 2 – Regression
• Simple Linear Regression
• How to get the dataset
• Dataset + Business Problem Description
• Simple Linear Regression Intuition – Step 1
• Simple Linear Regression Intuition – Step 2
• Simple Linear Regression in Python – Step 1
• Simple Linear Regression in Python – Step 2
• Simple Linear Regression in Python – Step 3
• Simple Linear Regression in Python – Step 4
• Simple Linear Regression in R – Step 1
• Simple Linear Regression in R – Step 2Simple Linear Regression in R – Step 3
• Simple Linear Regression in R – Step 4
• Simple Linear Regression
• Multiple Linear Regression
• How to get the dataset
• Dataset + Business Problem Description
• Multiple Linear Regression Intuition – Step 1
• Multiple Linear Regression Intuition – Step 2
• Multiple Linear Regression Intuition – Step 3
• Multiple Linear Regression Intuition – Step 4
• Multiple Linear Regression Intuition – Step 5
• Multiple Linear Regression in Python – Step 1
• Multiple Linear Regression in Python – Step 2
• Multiple Linear Regression in Python – Step 3
• Multiple Linear Regression in Python – Backward Elimination – Preparation
• Multiple Linear Regression in Python – Backward Elimination – HOMEWORK
• Multiple Linear Regression in Python – Backward Elimination – Homework Solution
• Multiple Linear Regression in R – Step 1
• Multiple Linear Regression in R – Step 2
• Multiple Linear Regression in R – Step 3
• Multiple Linear Regression in R – Backward Elimination – HOMEWORK
• Multiple Linear Regression in R – Backward Elimination – Homework Solution
• Multiple Linear Regression
• Polynomial Regression
• Polynomial Regression Intuition
• How to get the dataset
• Polynomial Regression in Python – Step 1
• Polynomial Regression in Python – Step 2
• Polynomial Regression in Python – Step 3
• Polynomial Regression in Python – Step 4
• Python Regression Template
• Polynomial Regression in R – Step 1
• Polynomial Regression in R – Step 2
• Polynomial Regression in R – Step 3
• Polynomial Regression in R – Step 4
• R Regression Template
• Support Vector Regression (SVR)
• How to get the dataset
• SVR in Python
• SVR in R
• Decision Tree Regression
• Decision Tree Regression Intuition
• How to get the dataset
• Decision Tree Regression in Python
• Decision Tree Regression in R
• Random Forest Regression
• Random Forest Regression Intuition
• How to get the dataset
• Random Forest Regression in Python
• Random Forest Regression in R
• Evaluating Regression Models Performance
• R-Squared Intuition
• Adjusted R-Squared Intuition
• Evaluating Regression Models Performance – Homework’s Final Part
• Interpreting Linear Regression Coefficients
• Conclusion of Part 2 – Regression

Part 3: Classification

• Welcome to Part 3 – Classification
• Logistic Regression
• Logistic Regression Intuition
• How to get the dataset
• Logistic Regression in Python – Step 1
• Logistic Regression in Python – Step 2
• Logistic Regression in Python – Step 3
• Logistic Regression in Python – Step 4
• Logistic Regression in Python – Step 5
• Python Classification Template
• Logistic Regression in R – Step 1
• Logistic Regression in R – Step 2
• Logistic Regression in R – Step 3
• Logistic Regression in R – Step 4
• Logistic Regression in R – Step 5
• R Classification Template
• Logistic Regression
• K-Nearest Neighbors (K-NN)
• K-Nearest Neighbor Intuition
• How to get the dataset
• K-NN in Python
• K-NN in R
• K-Nearest Neighbor
• Support Vector Machine (SVM)
• SVM Intuition
• How to get the dataset
• SVM in Python
• SVM in R
• Kernel SVM
• Kernel SVM Intuition
• Mapping to a higher dimension
• The Kernel Trick
• Types of Kernel Functions
• How to get the dataset
• Kernel SVM in Python
• Kernel SVM in R
• Naive Bayes
• Bayes Theorem
• Naive Bayes Intuition
• Naive Bayes Intuition (Challenge Reveal)
• Naive Bayes Intuition (Extras)
• How to get the dataset
• Naive Bayes in Python
• Naive Bayes in R
• Decision Tree Classification
• Decision Tree Classification Intuition
• How to get the dataset
• Decision Tree Classification in Python
• Decision Tree Classification in R
• Random Forest Classification
• Random Forest Classification Intuition
• How to get the dataset
• Random Forest Classification in Python
• Random Forest Classification in R
• Evaluating Classification Models Performance
• False Positives & False Negatives
• Confusion Matrix
• Accuracy Paradox
• CAP Curve
• CAP Curve Analysis
• Conclusion of Part 3 – Classification

Part 4: Clustering

• Welcome to Part 4 – Clustering
• K-Means Clustering
• K-Means Clustering Intuition
• K-Means Random Initialization Trap
• K-Means Selecting The Number Of Clusters
• How to get the dataset
• K-Means Clustering in Python
• K-Means Clustering in R
• K-Means Clustering
• Hierarchical Clustering
• Hierarchical Clustering Intuition
• Hierarchical Clustering How Dendrograms Work
• Hierarchical Clustering Using Dendrograms
• How to get the dataset
• HC in Python – Step 1
• HC in Python – Step 2
• HC in Python – Step 3
• HC in Python – Step 4
• HC in Python – Step 5
• HC in R – Step 1
• HC in R – Step 2
• HC in R – Step 3
• HC in R – Step 4
• HC in R – Step 5
• Hierarchical Clustering
• Conclusion of Part 4 – Clustering

Part 5: Association Rule Learning

• Welcome to Part 5 – Association Rule Learning
• Apriori
• Apriori Intuition
• How to get the dataset
• Apriori in R – Step 1
• Apriori in R – Step 2
• Apriori in R – Step 3
• Apriori in Python – Step 1
• Apriori in Python – Step 2
• Apriori in Python – Step 3
• Eclat
• Eclat Intuition
• How to get the dataset
• Eclat in R

Part 6: Reinforcement Learning

• Welcome to Part 6 – Reinforcement Learning
• Upper Confidence Bound (UCB)
• The Multi-Armed Bandit Problem
• Upper Confidence Bound (UCB) Intuition
• How to get the dataset
• Upper Confidence Bound in Python – Step 1
• Upper Confidence Bound in Python – Step 2
• Upper Confidence Bound in Python – Step 3
• Upper Confidence Bound in Python – Step 4
• Upper Confidence Bound in R – Step 1
• Upper Confidence Bound in R – Step 2
• Upper Confidence Bound in R – Step 3
• Upper Confidence Bound in R – Step 4
• Thompson Sampling
• Thompson Sampling Intuition
• Algorithm Comparison: UCB vs Thompson Sampling
• How to get the dataset
• Thompson Sampling in Python – Step 1
• Thompson Sampling in Python – Step 2
• Thompson Sampling in R – Step 1
• Thompson Sampling in R – Step 2

Part 7: Natural Language Processing

• Welcome to Part 7 – Natural Language Processing
• How to get the dataset
• Natural Language Processing in Python – Step 1
• Natural Language Processing in Python – Step 2
• Natural Language Processing in Python – Step 3
• Natural Language Processing in Python – Step 4
• Natural Language Processing in Python – Step 5
• Natural Language Processing in Python – Step 6
• Natural Language Processing in Python – Step 7
• Natural Language Processing in Python – Step 8
• Natural Language Processing in Python – Step 9
• Natural Language Processing in Python – Step 10
• Homework Challenge
• Natural Language Processing in R – Step 1
• Natural Language Processing in R – Step 2
• Natural Language Processing in R – Step 3
• Natural Language Processing in R – Step 4
• Natural Language Processing in R – Step 5
• Natural Language Processing in R – Step 6
• Natural Language Processing in R – Step 7
• Natural Language Processing in R – Step 8
• Natural Language Processing in R – Step 9
• Natural Language Processing in R – Step 10
• Homework Challenge

Part 8: Deep Learning

• Welcome to Part 8 – Deep Learning
• What is Deep Learning?
• Artificial Neural Networks
• Plan of attack
• The Neuron
• The Activation Function
• How do Neural Networks work?
• How do Neural Networks learn?
• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• How to get the dataset
• Business Problem Description
• ANN in Python – Step 1 – Installing Theano, Tensorflow and Keras
• ANN in Python – Step 2
• ANN in Python – Step 3
• ANN in Python – Step 4
• ANN in Python – Step 5
• ANN in Python – Step 6
• ANN in Python – Step 7
• ANN in Python – Step 8
• ANN in Python – Step 9
• ANN in Python – Step 10
• ANN in R – Step 1
• ANN in R – Step 2
• ANN in R – Step 3
• ANN in R – Step 4 (Last step)
• Convolutional Neural Networks
• Plan of attack
• What are convolutional neural networks?
• Step 1 – Convolution Operation
• Step 1(b) – ReLU Layer
• Step 2 – Pooling
• Step 3 – Flattening
• Step 4 – Full Connection
• Summary
• Softmax & Cross-Entropy
• How to get the dataset
• CNN in Python – Step 1
• CNN in Python – Step 2
• CNN in Python – Step 3
• CNN in Python – Step 4
• CNN in Python – Step 5
• CNN in Python – Step 6
• CNN in Python – Step 7
• CNN in Python – Step 8
• CNN in Python – Step 9
• CNN in Python – Step 10
• CNN in R

Part 9: Dimensionality Reduction

• Welcome to Part 9 – Dimensionality Reduction
• Principal Component Analysis (PCA)
• How to get the dataset
• PCA in Python – Step 1
• PCA in Python – Step 2
• PCA in Python – Step 3
• PCA in R – Step 1
• PCA in R – Step 2
• PCA in R – Step 3
• Linear Discriminant Analysis (LDA)
• How to get the dataset
• LDA in Python
• LDA in R
• Kernel PCA
• How to get the dataset
• Kernel PCA in Python
• Kernel PCA in R

Part 10: Model Selection & Boosting

• Welcome to Part 10 – Model Selection & Boosting
• Model Selection
• How to get the dataset
• k-Fold Cross Validation in Python
• k-Fold Cross Validation in R
• Grid Search in Python – Step 1
• Grid Search in Python – Step 2
• Grid Search in R
• XGBoost
• How to get the dataset
• XGBoost in Python – Step 1
• XGBoost in Python – Step 2
• XGBoost in R
• Bonus Lectures

• 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

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https://www.udemy.com/machinelearning/

Kirill Eremenko teaches courses in two distinct Business areas on Udemy: Data Science and Forex Trading. He is a Data Science management consultant with over five years of experience in finance, retail, transport and other industries, trained at Deloitte Australia. He now leverages Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

Hadelin de Ponteves is a consultant in the field of Machine Learning, Deep Learning and Artificial Intelligence. He holds an engineering master’s degree with a specialisation in Data Science, worked for Google and eventually became an entrepreneur.