Free Machine Learning Course with Certificate

Machine Learning

• Lesson 01: Course Introduction

  09:19

  • 1.01 Course Introduction

    06:08

  • 1.02 Demo: Jupyter Lab Walk - Through

    03:11

• Lesson 02: Introduction to Machine Learning

  08:40

  • 2.01 Learning Objectives

    00:42

  • 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

    02:46

  • 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

    01:23

  • 2.04 Definition and Features of Machine Learning

    01:30

  • 2.05 Machine Learning Approaches

    01:46

  • 2.06 Key Takeaways

    00:33

• Lesson 03: Supervised Learning Regression and Classification

  02:10:59

  • 3.01 Learning Objectives

    00:46

  • 3.02 Supervised Learning

    02:18

  • 3.03 Supervised Learning: Real Life Scenario

    00:55

  • 3.04 Understanding the Algorithm

    00:54

  • 3.05 Supervised Learning Flow

    01:51

  • 3.06 Types of Supervised Learning: Part A

    01:57

  • 3.07 Types of Supervised Learning: Part B

    02:05

  • 3.08 Types of Classification Algorithms

    01:03

  • 3.09 Types of Regression Algorithms: Part A

    03:23

  • 3.10 Regression Use Case

    00:36

  • 3.11 Accuracy Metrics

    01:24

  • 3.12 Cost Function

    01:49

  • 3.13 Evaluating Coefficients

    00:55

  • 3.14 Demo: Linear Regression

    13:48

  • 3.15 Challenges in Prediction

    01:47

  • 3.16 Types of Regression Algorithms: Part B

    02:40

  • 3.17 Demo: Bigmart

    37:29

  • 3.18 Logistic Regression: Part A

    02:01

  • 3.19 Logistic Regression: Part B

    01:41

  • 3.20 Sigmoid Probability

    02:07

  • 3.21 Accuracy Matrix

    01:28

  • 3.22 Demo: Survival of Titanic Passengers

    13:17

  • 3.23 Overview of Classification

    02:03

  • 3.24 Classification: A Supervised Learning Algorithm

    00:52

  • 3.25 Use Cases

    02:34

  • 3.26 Classification Algorithms

    00:17

  • 3.27 Performance Measures: Confusion Matrix

    02:21

  • 3.28 Performance Measures: Cost Matrix

    02:07

  • 3.29 Naive Bayes Classifier

    01:16

  • 3.30 Steps to Calculate Posterior Probability: Part A

    01:41

  • 3.31 Steps to Calculate Posterior Probability: Part B

    02:22

  • 3.32 Support Vector Machines: Linear Separability

    01:05

  • 3.33 Support Vector Machines: Classification Margin

    02:06

  • 3.34 Linear SVM: Mathematical Representation

    02:05

  • 3.35 Non linear SVMs

    01:07

  • 3.36 The Kernel Trick

    01:19

  • 3.37 Demo: Voice Classification

    10:42

  • 3.38 Key Takeaways

    00:48

• Lesson 04: Decision Trees and Random Forest

  18:09

  • 4.01 Learning Objectives

    00:37

  • 4.02 Decision Tree: Classifier

    02:17

  • 4.03 Decision Tree: Examples

    01:44

  • 4.04 Decision Tree: Formation

    00:46

  • 4.05 Choosing the Classifier

    02:56

  • 4.06 Overfitting of Decision Trees

    01:01

  • 4.07 Random Forest Classifier Bagging and Bootstrapping

    02:19

  • 4.08 Decision Tree and Random Forest Classifier

    01:07

  • 4.09 Demo: Horse Survival

    04:57

  • 4.10 Key Takeaways

    00:25

• Lesson 05: Unsupervised Learning

  32:41

  • 5.01 Learning Objectives

    00:36

  • 5.02 Overview

    01:47

  • 5.03 Example and Applications of Unsupervised Learning

    02:17

  • 5.04 Clustering

    01:46

  • 5.05 Hierarchical Clustering

    02:30

  • 5.06 Hierarchical Clustering: Example

    02:02

  • 5.07 Demo: Clustering Animals

    05:40

  • 5.08 K-means Clustering

    03:54

  • 5.09 Optimal Number of Clusters

    03:27

  • 5.10 Demo: Cluster Based Incentivization

    08:18

  • 5.11 Key Takeaways

    00:24

• Lesson 06: Time Series Modelling

  38:57

  • 6.01 Learning Objectives

    00:24

  • 6.02 Overview of Time Series Modeling

    02:16

  • 6.03 Time Series Pattern Types: Part A

    02:16

  • 6.04 Time Series Pattern Types: Part B

    01:19

  • 6.05 White Noise

    01:06

  • 6.06 Stationarity

    02:13

  • 6.07 Removal of Non Stationarity

    02:13

  • 6.08 Demo: Air Passengers I

    14:26

  • 6.09 Time Series Models: Part A

    02:14

  • 6.10 Time Series Models: Part B

    01:28

  • 6.11 Time Series Models: Part C

    01:51

  • 6.12 Steps in Time Series Forecasting

    00:37

  • 6.13 Demo: Air Passengers II

    06:14

  • 6.14 Key Takeaways

    00:20

• Lesson 07: Ensemble Learning

  39:35

  • 7.01 Learning Objectives

    00:24

  • 7.02 Overview

    02:41

  • 7.03 Ensemble Learning Methods: Part A

    02:49

  • 7.04 Ensemble Learning Methods: Part B

    04:09

  • 7.05 Working of AdaBoost

    01:43

  • 7.06 AdaBoost Algorithm and Flowchart

    02:28

  • 7.07 Gradient Boosting

    04:37

  • 7.08 XGBoost

    02:23

  • 7.09 XGBoost Parameters: Part A

    03:15

  • 7.10 XGBoost Parameters: Part B

    02:30

  • 7.11 Demo: Pima Indians Diabetes

    03:11

  • 7.12 Model Selection

    02:55

  • 7.13 Common Splitting Strategies

    01:45

  • 7.14 Demo: Cross Validation

    04:18

  • 7.15 Key Takeaways

    00:27

• Lesson 08: Recommender Systems

  26:11

  • 8.01 Learning Objectives

    00:27

  • 8.02 Introduction

    02:16

  • 8.03 Purposes of Recommender Systems

    00:45

  • 8.04 Paradigms of Recommender Systems

    02:45

  • 8.05 Collaborative Filtering: Part A

    02:14

  • 8.06 Collaborative Filtering: Part B

    01:58

  • 8.07 Association Rule: Mining

    01:47

  • 8.08 Association Rule: Mining Market Basket Analysis

    01:42

  • 8.09 Association Rule: Generation Apriori Algorithm

    00:53

  • 8.10 Apriori Algorithm Example: Part A

    02:13

  • 8.11 Apriori Algorithm Example: Part B

    01:17

  • 8.12 Apriori Algorithm: Rule Selection

    02:52

  • 8.13 Demo: User Movie Recommendation Model

    04:12

  • 8.14 Key Takeaways

    00:50

• Lesson 09: Level Up Sessions

  10:31

  • Session 01

    05:22

  • Session 02

    05:09

• Practice Project

  • California Housing Price Prediction

  • Phishing Detector with LR