Module 1 Section 1: Welcome to Machine Learning A-Z
Unit 1 Welcome to the course!
Unit 2 Applications of Machine Learning
Unit 3 Why Machine Learning is the Future
Unit 4 Installing R and R Studio (MAC & Windows)
Unit 5 Installing Python and Anaconda (MAC & Windows)
Module 2 Section 2: ---------------------- Part 1 - Data Processing ----------------------
Unit 1 Welcome to Part 1 - Data Preprocessing
Unit 2 Get the dataset
Unit 3 Importing the Libraries
Unit 4 Importing the Dataset
Unit 5 Missing Data
Unit 6 Categorical Data
Unit 7 Splitting the Dataset into the Training set and Test set
Unit 8 Feature Scaling
Unit 9 And here is our Data Preprocessing Template !
Unit 10 Quiz 1 - Data Preprocessing
Module 3 Section 3: ---------------------- Part 2 - Regression ----------------------
There are no units in this module.
Module 4 Section 4: Simple Linear Regression
Unit 1 Dataset + Business Problem Description
Unit 2 Simple Linear Regression Intuition - Step 1
Unit 3 Simple Linear Regression Intuition - Step 2
Unit 4 Simple Linear Regression in Python - Step 1
Unit 5 Simple Linear Regression in Python - Step 2
Unit 6 Simple Linear Regression in Python - Step 3
Unit 7 Simple Linear Regression in Python - Step 4
Unit 8 Simple Linear Regression in R - Step 1
Unit 9 Simple Linear Regression in R - Step 2
Unit 10 Simple Linear Regression in R - Step 3
Unit 11 Simple Linear Regression in R - Step 4
Unit 12 Quiz 2 - Simple Linear Regression
Module 5 Section 5: Multiple Linear Regression
Unit 1 Dataset + Business Problem Description
Unit 2 Multiple Linear Regression Intuition - Step 1
Unit 3 Multiple Linear Regression Intuition - Step 2
Unit 4 Multiple Linear Regression Intuition - Step 3
Unit 5 Multiple Linear Regression Intuition - Step 4
Unit 6 Multiple Linear Regression Intuition - Step 5
Unit 7 Multiple Linear Regression in Python - Step 1
Unit 8 Multiple Linear Regression in Python - Step 2
Unit 9 Multiple Linear Regression in Python - Step 3
Unit 10 Multiple Linear Regression in Python - Backward Elimination - Preparation
Unit 11 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
Unit 12 Multiple Linear Regression in Python - Backward Elimination - Homework Solution
Unit 13 Multiple Linear Regression in R - Step 1
Unit 14 Multiple Linear Regression in R - Step 2
Unit 15 Multiple Linear Regression in R - Step 3
Unit 16 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
Unit 17 Multiple Linear Regression in R - Backward Elimination - Homework Solution
Unit 18 Quiz 3 - Multiple Linear Regression
Module 6 Section 6: Polynomial Regression
Unit 1 Polynomial Regression Intuition
Unit 2 Polynomial Regression in Python - Step 1
Unit 3 Polynomial Regression in Python - Step 2
Unit 4 Polynomial Regression in Python - Step 3
Unit 5 Polynomial Regression in Python - Step 4
Unit 6 Python Regression Template
Unit 7 Polynomial Regression in R - Step 1
Unit 8 Polynomial Regression in R - Step 2
Unit 9 Polynomial Regression in R - Step 3
Unit 10 Polynomial Regression in R - Step 4
Unit 11 R Regression Template
Module 7 Section 7: Support Vector Regression (SVR)
Unit 1 SVR in Python
Unit 2 SVR in R
Module 8 Section 8: Decision Tree Regression
Unit 1 Decision Tree Regression Intuition
Unit 2 Random Forest Regression in Python
Unit 3 Decision Tree Regression in R
Module 9 Section 9: Random Forest Regression
Unit 1 Random Forest Regression Intuition
Unit 2 Random Forest Regression in Python
Unit 3 Random Forest Regression in R
Module 10 Section 10: Evaluating Regression Models Performance
Unit 1 R-Squared Intuition
Unit 2 Adjusted R-Squared Intuition
Unit 3 Evaluating Regression Models Performance - Homework's Final Part
Unit 4 Interpreting Linear Regression Coefficients
Module 11 Section 11: Regularization Methods
There are no units in this module.
Module 12 Section 12: Part Recap
There are no units in this module.
Module 13 Section 13: ---------------------- Part 3: Classification ----------------------
There are no units in this module.
Module 14 Section 14: Logistic Regression
Unit 1 Logistic Regression Intuition
Unit 2 Logistic Regression in Python - Step 1
Unit 3 Logistic Regression in Python - Step 2
Unit 4 Logistic Regression in Python - Step 3
Unit 5 Logistic Regression in Python - Step 4
Unit 6 Logistic Regression in Python - Step 5
Unit 7 Python Classification Template
Unit 8 Logistic Regression in R - Step 1
Unit 9 Logistic Regression in R - Step 2
Unit 10 Logistic Regression in R - Step 3
Unit 11 Logistic Regression in R - Step 4
Unit 12 Logistic Regression in R - Step 5
Unit 13 R Classification Template
Unit 14 Quiz 4 – Logistic Regression
Module 15 Section 15: k-Nearest Neighbors (k-NN)
Unit 1 K-Nearest Neighbor Intuition
Unit 2 K-NN in Python
Unit 3 K-NN in R
Unit 4 Quiz 5 - K-Nearest Neighbor
Module 16 Section 16: Support Vector Machine (SVM)
Unit 1 SVM Intuition
Unit 2 SVM in Python
Unit 3 SVM in R
Module 17 Section 17: Kernel SVM
Unit 1 Kernel SVM Intuition
Unit 2 Mapping to a higher dimension
Unit 3 The Kernel Trick
Unit 4 Types of Kernel Functions
Unit 5 Kernel SVM in Python
Unit 6 Kernel SVM in R
Module 18 Section 18: Naive Bayes
Unit 1 Bayes Theorem
Unit 2 Naive Bayes Intuition
Unit 3 Naive Bayes Intuition (Challenge Reveal)
Unit 4 Naive Bayes Intuition (Extras)
Unit 5 Naive Bayes in Python
Unit 6 Naive Bayes in R
Module 19 Section 19: Decision Tree Classification
Unit 1 Decision Tree Classification Intuition
Unit 2 Decision Tree Classification in Python
Unit 3 Decision Tree Classification in R
Module 20 Section 20: Random Forest Classification
Unit 1 Random Forest Classification Intuition
Unit 2 Random Forest Classification in Python
Unit 3 Random Forest Classification in R
Module 21 Section 21: Evaluating Classification Models Performance
Unit 1 False Positives & False Negatives
Unit 2 Confusion Matrix
Unit 3 Accuracy Paradox
Unit 4 CAP Curve
Unit 5 CAP Curve Analysis
Module 22 Section 22: Part Recap
There are no units in this module.
Module 23 Section 23: ---------------------------- Part 4: Clustering ----------------------------
There are no units in this module.
Module 24 Section 24: K-Means Clustering
Unit 1 K-Means Clustering Intuition
Unit 2 K-Means Random Initialization Trap
Unit 3 K-Means Selecting The Number Of Clusters
Unit 4 K-Means Clustering in Python
Unit 5 K-Means Clustering in R
Unit 6 Quiz 6 - K-Means Clustering
Module 25 Section 25: Hierarchical Clustering
Unit 1 Hierarchical Clustering Intuition
Unit 2 Hierarchical Clustering How Dendrograms Work
Unit 3 Hierarchical Clustering Using Dendrograms
Unit 4 HC in Python - Step 1
Unit 5 HC in Python - Step 2
Unit 6 HC in Python - Step 3
Unit 7 HC in Python - Step 4
Unit 8 HC in Python - Step 5
Unit 9 HC in R - Step 1
Unit 10 HC in R - Step 2
Unit 11 HC in R - Step 3
Unit 12 HC in R - Step 4
Unit 13 HC in R - Step 5
Unit 14 Quiz 7 - Hierarchical Clustering
Module 26 Section 26: Part Recap
There are no units in this module.
Module 27 Section 27: ---------------------------- Part 5: Association Rule Learning ----------------------------
There are no units in this module.
Module 28 Section 28: Apriori
Unit 1 Apriori Intuition
Unit 2 Apriori in R - Step 1
Unit 3 Apriori in R - Step 2
Unit 4 Apriori in R - Step 3
Unit 5 Apriori in Python - Step 1
Unit 6 Apriori in Python - Step 2
Unit 7 Apriori in Python - Step 3
Module 29 Section 29: Eclat
Unit 1 Eclat Intuition
Unit 2 Eclat in R
Module 30 Section 30 - Part Recap
There are no units in this module.
Module 31 Section 31: ---------------------------- Part 6: Reinforcement Learning ----------------------------
There are no units in this module.
Module 32 Section 32: Upper Confidence Bound (UCB)
Unit 1 The Multi-Armed Bandit Problem
Unit 2 Upper Confidence Bound (UCB) Intuition
Unit 3 Upper Confidence Bound in Python - Step 1
Unit 4 Upper Confidence Bound in Python - Step 2
Unit 5 Upper Confidence Bound in Python - Step 3
Unit 6 Upper Confidence Bound in Python - Step 4
Unit 7 Upper Confidence Bound in R - Step 1
Unit 8 Upper Confidence Bound in R - Step 2
Unit 9 Upper Confidence Bound in R - Step 3
Unit 10 Upper Confidence Bound in R - Step 4
Module 33 Section 33: Thompson Sampling
Unit 1 Thompson Sampling Intuition
Unit 2 Algorithm Comparison: UCB vs Thompson Sampling
Unit 3 Thompson Sampling in Python - Step 1
Unit 4 Thompson Sampling in Python - Step 2
Unit 5 Thompson Sampling in R - Step 1
Unit 6 Thompson Sampling in R - Step 2
Module 34 Section 34: Part Recap
There are no units in this module.
Module 35 Section 35: ---------------------------- Part 7: Natural Language Processing ----------------------------
There are no units in this module.
Module 36 Section 36: Natural Language Processing
Unit 1 Natural Language Processing in Python - Step 1
Unit 2 Natural Language Processing in Python - Step 2
Unit 3 Natural Language Processing in Python - Step 3
Unit 4 Natural Language Processing in Python - Step 4
Unit 5 Natural Language Processing in Python - Step 5
Unit 6 Natural Language Processing in Python - Step 6
Unit 7 Natural Language Processing in Python - Step 7
Unit 8 Natural Language Processing in Python - Step 8
Unit 9 Natural Language Processing in Python - Step 9
Unit 10 Natural Language Processing in Python - Step 10
Unit 11 Natural Language Processing in R - Step 1
Unit 12 Natural Language Processing in R - Step 2
Unit 13 Natural Language Processing in R - Step 3
Unit 14 Natural Language Processing in R - Step 4
Unit 15 Natural Language Processing in R - Step 5
Unit 16 Natural Language Processing in R - Step 6
Unit 17 Natural Language Processing in R - Step 7
Unit 18 Natural Language Processing in R - Step 8
Unit 19 Natural Language Processing in R - Step 9
Unit 20 Natural Language Processing in R - Step 10
Module 37 Section 37: Part Recap
There are no units in this module.
Module 38 Section 38: ----------------------------Part 8 - Deep Learning ----------------------------
There are no units in this module.
Module 39 Section 39: Artificial Neural Networks (ANN)
Unit 1 Business Problem Description
Unit 2 ANN in Python - Step 1 - Installing Theano, Tensorflow and Keras
Unit 3 ANN in Python - Step 2
Unit 4 ANN in Python - Step 3
Unit 5 ANN in Python - Step 4
Unit 6 ANN in Python - Step 5
Unit 7 ANN in Python - Step 6
Unit 8 ANN in Python - Step 7
Unit 9 ANN in Python - Step 8
Unit 10 ANN in Python - Step 9
Unit 11 ANN in Python - Step 10
Unit 12 ANN in R - Step 1
Unit 13 ann in R 2
Unit 14 ANN in R - Step 3
Module 40 Section 40: Convolutional Neural Networks (CNN)
Unit 1 CNN in Python - Step 1
Unit 2 CNN in Python - Step 2
Unit 3 CNN in Python - Step 3
Unit 4 CNN in Python - Step 4
Unit 5 CNN in Python - Step 5
Unit 6 CNN in Python - Step 6
Unit 7 CNN in Python - Step 7
Unit 8 CNN in Python - Step 8
Unit 9 CNN in Python - Step 9
Unit 10 CNN in Python - Step 10
Module 41 Section 41: Part Recap
There are no units in this module.
Module 42 Section 42: ---------------------------- Part 9: Dimensionality Reduction ----------------------------
There are no units in this module.
Module 43 Section 43: Principal Component Analysis (PCA)
Unit 1 PCA in Python - Step 1
Unit 2 PCA in Python - Step 2
Unit 3 PCA in Python - Step 3
Unit 4 PCA in R - Step 1
Unit 5 PCA in R - Step 2
Unit 6 PCA in R - Step 3
Module 44 Section 44: Linear Discriminant Analysis (LDA)
Unit 1 LDA in Python
Unit 2 LDA in R
Module 45 Section 45: Kernel PCA
Unit 1 Kernel PCA in Python
Unit 2 Kernel PCA in R
Module 46 Section 46: Part Recap
There are no units in this module.
Module 47 Section 47: -------------------- Part 10 - Model Selection & Boosting --------------------
There are no units in this module.
Module 48 Section 48: Model Selection
Unit 1 k-Fold Cross Validation in Python
Unit 2 k-Fold Cross Validation in R
Unit 3 Grid Search in Python - Step 1
Unit 4 Grid Search in Python - Step 2
Unit 5 Grid Search in R
Module 49 Section 49 - XGBoost
Unit 1 XGBoost in Python - Step 1
Unit 2 XGBoost in Python - Step 2
Unit 3 XGBoost in R
Module 50 Section 50: Part Recap
There are no units in this module.
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Machine Learning

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Course Description

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

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

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 is structured in a fun and exciting way, but at the same time we dive deep into Machine Learning. In this course you will learn the following algorithms:

  • Linear Regression
  • Multiple Linear Regression
  • K-Means Clustering
  • Hierarchical Clustering
  • K-Nearest Neighbour
  • Decision Trees
  • Random Forest

Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both R and Python code templates which you can download and use on your own projects.