Machine Learning Course Free with Certificate

Machine Learning using Python

• Lesson 01 - Course Introduction

  • 1.01 Course Introduction

    02:39

  • 1.02 What You Will Learn

    01:55

• Lesson 02 - Introduction to Machine Learning

  • 2.01 Introduction

    00:51

  • 2.02 What Is Machine Learning?

    02:49

  • 2.03 Types of Machine Learning

    02:54

  • 2.04 Machine Learning Pipeline and MLOP's

    03:35

  • 2.05 Introduction to Python Packages Used in Machine Learning

    03:29

  • 2.06 Recap

    00:52

• Lesson 03 - Supervised Learning

  • 3.01 Introduction

    00:41

  • 3.02 Supervised Learning

    02:34

  • 3.03 Applications of Supervised Learning

    03:11

  • 3.04 Preparing and Shaping Data

    06:50

  • 3.05 What is overfitting and underfitting?

    02:23

  • 3.06 Detecting and Preventing Overfitting and Underfitting

    07:36

  • 3.07 Regularization

    02:38

  • 3.08 Recap

    00:41

• Lesson 04 - Regression and Applications

  • 4.01 Introduction

    01:14

  • 4.02 What is Regression?

    01:34

  • 4.03 Regression Types: Introduction

    02:45

  • 4.04 Linear Regression

    02:48

  • 4.05 Working with Linear Regression

    35:14

  • 4.06 Critical Assumptions for Linear Regression

    01:31

  • 4.07 Logistic Regression

    02:33

  • 4.08 Data Exploration Using SMOTE

    12:56

  • 4.09 Over Sampling Using SMOTE

    01:48

  • 4.10 Polynomial Regression

    02:41

  • 4.11 Data Preparation Model Building and Performance Evaluation Part A

    04:53

  • 4.12 Ridge Regression

    01:57

  • 4.13 Data Preparation Model Building: Part B

    06:25

  • 4.14 LASSO Regression

    02:30

  • 4.15 Data Preparation Model Building: Part C

    06:13

  • 4.16 Recap

    00:55

  • 4.17 Spotlight

    02:40

• Lesson 05 - Classification and Applications

  • 5.01 Introduction

    01:03

  • 5.02 What are Classification Algorithms?

    02:09

  • 5.03 Types of Classification

    03:29

  • 5.04 Types and selection of performance parameters

    04:58

  • 5.05 Naive Bayes

    02:56

  • 5.06 Applying Naive Bayes Classifier

    03:27

  • 5.07 Stochastic Gradient Descent

    03:25

  • 5.08 Applying Stochastic Gradient Descent

    05:02

  • 5.09 K Nearest Neighbors

    02:41

  • 5.10 Applying K Nearest Neighbors

    05:28

  • 5.11 Decision Tree

    02:42

  • 5.12 Applying Decision Tree

    04:27

  • 5.13 Random Forest

    01:59

  • 5.14 Applying Random Forest

    03:22

  • 5.15 Boruta Explained

    01:15

  • 5.16 Automatic Feature Selection with Boruta

    06:43

  • 5.17 Support Vector Machine

    02:27

  • 5.18 Applying Support Vector Machine

    07:07

  • 5.19 Cohens Kappa Measure

    01:22

  • 5.20 Recap

    00:42

• Lesson 06 - Unsupervised Algorithms

  • 6.01 Introduction

    00:53

  • 6.02 What are Unsupervised Algorithms?

    02:51

  • 6.03 Types of Unsupervised Algorithms Clustering and Associative

    02:15

  • 6.04 When to Use Unsupervised Algorithms?

    01:22

  • 6.05 Visualizing Outputs

    06:14

  • 6.06 Performance Parameters

    02:55

  • 6.07 Clustering Types

    00:56

  • 6.08 Hierarchical Clustering

    03:32

  • 6.09 Applying Hierarchical Clustering

    03:22

  • 6.10 K means Clustering: Part 1

    02:30

  • 6.11 K means Clustering: Part 2

    01:54

  • 6.12 Applying K Means Clustering

    03:37

  • 6.13 KNN-K Nearest Neighbors

    03:41

  • 6.14 Outlier Detection

    01:47

  • 6.15 Outlier Detection Algorithms in PyOD

    02:49

  • 6.16 Demo: K NN for Anomaly Detection

    02:37

  • 6.17 Principal Component Analysis

    04:15

  • 6.18 Applying Principal Component Analysis: PCA

    04:21

  • 6.19 Correspondence Analysis Multiple correspondence analysis: MCA

    03:16

  • 6.20 Singular Value Decomposition

    02:06

  • 6.21 Applying Singular Value Decomposition

    04:14

  • 6.22 Independent Component Analysis

    02:26

  • 6.23 Applying Independent Component Analysis

    01:54

  • 6.24 BIRCH

    02:33

  • 6.25 Applying BIRCH

    02:15

  • 6.26 Recap

    01:05

  • 6.27 Spotlight

    03:20

• Lesson 07 - Ensemble Learning

  • 7.01 Introduction

    00:54

  • 7.02 What is Ensemble Learning?

    01:46

  • 7.03 Categories in Ensemble Learning

    02:47

  • 7.04 Sequential Ensemble Technique

    02:50

  • 7.05 Parallel Ensemble Technique

    02:10

  • 7.06 Types of Ensemble Methods

    01:56

  • 7.07 Bagging

    03:01

  • 7.08 Demo: Bagging

    02:53

  • 7.09 Boosting

    02:14

  • 7.10 Demo: Boosting

    03:29

  • 7.11 Stacking

    02:56

  • 7.12 Demo: Stacking

    03:44

  • 7.13 Reducing Errors with Ensembles

    05:27

  • 7.14 Applying Averaging and Max Voting

    06:33

  • 7.15 Hello World Tensorflow

    02:38

  • 7.16 Hands on with TensorFlow: Part A

    05:09

  • 7.17 Keras

    02:49

  • 7.18 Hands on with TensorFlow: Part B

    05:57

  • 7.19 Recap

    00:45

• Lesson 08 - Recommender System

  • 8.01 Introduction

    01:00

  • 8.02 How do recommendation engines work

    02:45

  • 8.03 Recommendation Engine: Use Cases

    01:44

  • 8.04 Examples of Recommender System and Their Designs

    02:55

  • 8.05 Leveraging PyTorch to Build a Recommendation Engine

    02:23

  • 8.06 Collaborative Filtering and Memory Based Modeling

    06:31

  • 8.07 Item Based Collaborative Filtering

    07:02

  • 8.08 User Based Collaborative Filtering

    13:05

  • 8.09 Model Based Collaborative Filtering

    04:09

  • 8.10 Dimensionality Reduction and Matrix Factorization

    04:51

  • 8.11 Accuracy Matrices in ML

    08:06

  • 8.12 Recap

    00:52

  • 8.13 Spotlight

    03:01