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# Machine Learning Certification Course

### Course Summary

SimplilearnÃ¢â‚¬â„¢s Machine Learning Course will introduce you to the world of Artificial Intelligence and Machine Learning. As part of the Machine Learning Certification training, you will master the concepts of Supervised Learning, Unsupervised Learning, Reinforcement Learning, Support Vector Machines, Kernel SVM, Naive Bayes, Decision tree classifier, Random forest classifier, Logistic regression, K-nearest neighbours, K-means Clustering to prepare you for the role of Machine Learning Engineer.

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### Course Syllabus

Course preview

Machine Learning

Lesson 1: Introduction to Artificial Intelligence and Machine Learning 15:45

Artificial Intelligence 15:45

Machine Learning

Machine Learning algorithms

Applications of Machine Learning

Lesson 2: Techniques of Machine Learning

Supervised Learning

Unsupervised Learning

Semi-supervised Learning and Reinforcement Learning

Some Important Considerations in Machine Learning

Lesson 3: Data Preprocessing

Data Preparation

Feature engineering

Feature scaling

Datasets

Dimensionality reduction

Lesson 4: Math Refresher

Eigenvalues, Eigenvectors, and Eigendecomposition

Concepts of Linear Algebra

Introduction to Calculus

Probability and Statistics

Lesson 5: Regression

Regression and Its Types

Linear Regression: Equations and Algorithms

Lesson 6: Classification

Classification

Logistic regression

K-nearest neighbours

Support Vector Machines

Kernel SVM

Naive Bayes

Decision tree classifier

Random forest classifier

Lesson 7: Unsupervised learning - Clustering

K-means Clustering

Clustering Algorithms

Lesson 8: Introduction to Deep Learning

Meaning and importance of deep learning

Artificial Neural networks

TensorFlow

Free Course Python Programming for Beginners

Section 1 - Getting Started with Python 20:58

1.1 Getting Started with Python 09:53

1.2 Print and Strings 08:11

1.3 Math 02:54

Section 2 - Variables, Loops and Statements 38:17

2.1 Variables, Loops and Statements 04:58

2.2 While Loops 06:13

2.3 For Loops 05:13

2.4 If Statments 06:59

2.5 If Else Statements 04:12

2.6 If Elif Else Statements 10:42

Section 3 - Functions and Variables 29:57

3.1 Functions And Variables 05:21

3.2 Function Parameters 15:00

3.3 Global And Local Variables 09:36

Section 4 - Understanding Error Detection 12:29

4.1 Understanding Error Detection 12:29

Section 5 - Working with Files and Classes 16:40

5.1 Working With Files And Classes 04:45

5.2 Appending To A File 03:29

5.3 Reading From A File 03:47

5.4 Classes 04:39

Section 6 - Intermediate Python 54:19

6.1 Intermediate Python 07:55

6.2 Import Syntax 06:53

6.3 Making Modules 06:39

6.4 Error Handling - Try And Accept 13:10

6.5 Lists vs Tuples And List Manipulation 11:03

6.6 Dictionaries 08:39

Section 7 - Conclusion 27:22

7.1 Conclusion 27:22

Free Course Learn Python Django From Scratch

Module 01 - Course Introduction 05:08

1.1 Course Introduction 04:10

1.2 Overview of Final Project 00:58

Module 02 - Introduction to Django 59:11

2.1 Introduction 00:35

2.2 Django Installation And Configuration 11:19

2.3 MVC Applied To Django Plus Git 08:19

2.4 Basic Views, Templates And Urls 15:37

2.5 Models, Databases, Migrations and the Django Admin 19:07

2.6 Section Recap 01:37

2.7 Quiz 02:37

Module 03 - Creating a User Authentication System 56:49

3.1 What You Will Learn In This Section 01:04

3.2 Setting Up A Simple User Authentication System 22:26

3.3 Login and Session Variables 18:40

3.4 Social Registration 13:29

3.5 Review 00:32

3.6 Quiz 00:38

Module 04 - Frontending 55:42

4.1 What You Will Learn In This Section 00:29

4.2 Template Language and Static Files 16:49

4.4 Static File Compression And Template Refactoring 17:05

4.5 Review 00:36

4.6 Quiz 00:26

Module 05 - E-Commerce 1:09:33

5.1 What You Will Learn In This Section 00:24

5.2 Preparing The Storefront 26:35

5.3 Adding A Shopping Cart 20:12

5.4 Paypal Integration 21:11

5.5 Stripe Integration With Ajax 00:01

5.6 Review 00:41

5.7 Quiz 00:29

6.1 What You Will Learn In This Section 00:37

6.3 Forms 13:19

6.5 Review 00:38

6.6 Quiz 00:25

Module 07 - Geolocation and Map Integration 18:36

7.1 What You Will Learn In This Section 00:37

7.2 Adding A Map Representation With Geolocation 08:35

7.4 Review 00:31

7.5 Quiz 00:29

Module 08 - Django Power-Ups Services and Signals 20:11

8.1 What You Will Learn In This Section 00:52

8.2 Building A Web Service With Tastypie 11:04

8.3 Signals 08:15

Module 09 - Testing Your Site 36:20

9.1 What You Will Learn In This Section 00:21

9.2 Adding The Django Debug Toolbar 04:36

9.3 Unit Testing 18:05

9.4 Logging 12:14

9.5 Review 00:40

9.6 Quiz 00:24

Module 10 - Course Conclusion 04:55

10.1 Conclusion 04:55

Free Course Python Game Development - Create a Flappy Bird Clone

Python Game Development - Create a Flappy Bird Clone 2:57:17

1.1 Introduction to the Course and the Game 03:08

1.2 Introduction to PyGame and Initial Coding 09:04

1.3 Time Clock and Game Over 10:24

1.4 Graphics Setup 02:59

1.5 Background and Adding Graphics to the Screen 06:06

1.6 Working with Coordinates 06:02

1.7 Creating Input Controls 11:17

1.8 Boundaries, Crash Events and Menu Creation 09:47

1.9 Part 2 09:37

1.10 Part 3 06:56

1.11 Part 4 07:58

1.12 Creating Obstacles Using Polygons 07:38

1.13 Completing Our Obstacles 09:08

1.14 Game Logic Using Block Logic 12:43

1.15 Game Logic Success Or Failure 12:19

1.16 Hitting Obstacles Part 2 05:11

1.17 Creating the Score Display 12:00

1.18 Adding Colors and Difficulty Levels 12:27

1.19 Adding Colors Part 2 12:53

Free Course Data Science with Python

Lesson 00 - Course Overview 04:34

0.1 Course Overview 04:34

Lesson 01 - Data Science Overview 20:27

1.1 Introduction to Data Science 08:42

1.2 Different Sectors Using Data Science 05:59

1.3 Purpose and Components of Python 05:02

1.4 Quiz

1.5 Key Takeaways 00:44

Lesson 02 - Data Analytics Overview 18:20

2.1 Data Analytics Process 07:21

2.2 Knowledge Check

2.3 Exploratory Data Analysis(EDA)

2.4 EDA-Quantitative Technique

2.5 EDA - Graphical Technique 00:57

2.6 Data Analytics Conclusion or Predictions 04:30

2.7 Data Analytics Communication 02:06

2.8 Data Types for Plotting

2.9 Data Types and Plotting 02:29

2.10 Knowledge Check

2.11 Quiz

2.12 Key Takeaways 00:57

Lesson 03 - Statistical Analysis and Business Applications 23:53

3.1 Introduction to Statistics 01:31

3.2 Statistical and Non-statistical Analysis

3.3 Major Categories of Statistics 01:34

3.4 Statistical Analysis Considerations

3.5 Population and Sample 02:15

3.6 Statistical Analysis Process

3.7 Data Distribution 01:48

3.8 Dispersion

3.9 Knowledge Check

3.10 Histogram 03:59

3.11 Knowledge Check

3.12 Testing 08:18

3.13 Knowledge Check

3.14 Correlation and Inferential Statistics 02:57

3.15 Quiz

3.16 Key Takeaways 01:31

Lesson 04 - Python Environment Setup and Essentials 23:58

4.1 Anaconda 02:54

4.2 Installation of Anaconda Python Distribution (contd.)

4.3 Data Types with Python 13:28

4.4 Basic Operators and Functions 06:26

4.5 Quiz

4.6 Key Takeaways 01:10

Lesson 05 - Mathematical Computing with Python (NumPy) 30:31

5.1 Introduction to Numpy 05:30

5.2 Activity-Sequence it Right

5.3 Demo 01-Creating and Printing an ndarray 04:50

5.4 Knowledge Check

5.5 Class and Attributes of ndarray

5.6 Basic Operations 07:04

5.7 Activity-Slice It

5.8 Copy and Views

5.9 Mathematical Functions of Numpy 05:01

5.10 Assignment 01

5.11 Assignment 01 Demo 03:55

5.12 Assignment 02

5.13 Assignment 02 Demo 03:16

5.14 Quiz

5.15 Key Takeaways 00:55

Lesson 06 - Scientific computing with Python (Scipy) 23:35

6.1 Introduction to SciPy 06:57

6.2 SciPy Sub Package - Integration and Optimization 05:51

6.3 Knowledge Check

6.4 SciPy sub package

6.5 Demo - Calculate Eigenvalues and Eigenvector 01:36

6.6 Knowledge Check

6.7 SciPy Sub Package - Statistics, Weave and IO 05:46

6.8 Assignment 01

6.9 Assignment 01 Demo 01:20

6.10 Assignment 02

6.11 Assignment 02 Demo 00:55

6.12 Quiz

6.13 Key Takeaways 01:10

Lesson 07 - Data Manipulation with Pandas 47:34

7.1 Introduction to Pandas 12:29

7.2 Knowledge Check

7.3 Understanding DataFrame 05:31

7.4 View and Select Data Demo 05:34

7.5 Missing Values 03:16

7.6 Data Operations 09:56

7.7 Knowledge Check

7.8 File Read and Write Support 00:31

7.9 Knowledge Check-Sequence it Right

7.10 Pandas Sql Operation 02:00

7.11 Assignment 01

7.12 Assignment 01 Demo 04:09

7.13 Assignment 02

7.14 Assignment 02 Demo 02:34

7.15 Quiz

7.16 Key Takeaways 01:34

Lesson 08 - Machine Learning with ScikitÃ¢â‚¬â€œLearn 1:02:10

8.1 Machine Learning Approach 03:57

8.2 Steps 1 and 2 01:00

8.3 Steps 3 and 4

8.4 How it Works 01:24

8.5 Steps 5 and 6 01:54

8.6 Supervised Learning Model Considerations 00:30

8.7 Knowledge Check

8.8 Scikit-Learn 02:10

8.9 Knowledge Check

8.10 Supervised Learning Models - Linear Regression 11:19

8.11 Supervised Learning Models - Logistic Regression 08:43

8.12 Unsupervised Learning Models 10:40

8.13 Pipeline 02:37

8.14 Model Persistence and Evaluation 05:45

8.15 Knowledge Check

8.16 Assignment 01

8.17 Assignment 01 05:45

8.18 Assignment 02

8.19 Assignment 02 05:14

8.20 Quiz

8.21 Key Takeaways 01:12

Lesson 09 - Natural Language Processing with Scikit Learn 49:03

9.1 NLP Overview 10:42

9.2 NLP Applications

9.3 Knowledge check

9.4 NLP Libraries-Scikit 12:29

9.5 Extraction Considerations

9.6 Scikit Learn-Model Training and Grid Search 10:17

9.7 Assignment 01

9.8 Demo Assignment 01 06:32

9.9 Assignment 02

9.10 Demo Assignment 02 08:00

9.11 Quiz

9.12 Key Takeaway 01:03

Lesson 10 - Data Visualization in Python using matplotlib 32:46

10.1 Introduction to Data Visualization 08:02

10.2 Knowledge Check

10.3 Line Properties

10.4 (x,y) Plot and Subplots 10:01

10.5 Knowledge Check

10.6 Types of Plots 09:34

10.7 Assignment 01

10.8 Assignment 01 Demo 02:23

10.9 Assignment 02

10.10 Assignment 02 Demo 01:47

10.11 Quiz

10.12 Key Takeaways 00:59

Lesson 11 - Web Scraping with BeautifulSoup 52:27

11.1 Web Scraping and Parsing 12:50

11.2 Knowledge Check

11.3 Understanding and Searching the Tree 12:56

11.4 Navigating options

11.5 Demo3 Navigating a Tree 04:22

11.6 Knowledge Check

11.7 Modifying the Tree 05:38

11.8 Parsing and Printing the Document 09:05

11.9 Assignment 01

11.10 Assignment 01 Demo 01:55

11.11 Assignment 02

11.12 Assignment 02 demo 04:57

11.13 Quiz

11.14 Key takeaways 00:44

Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39

12.1 Why Big Data Solutions are Provided for Python 04:55

12.3 Python Integration with HDFS using Hadoop Streaming 07:20

12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count 08:52

12.5 Knowledge Check

12.6 Python Integration with Spark using PySpark 07:43

12.7 Demo 02 - Using PySpark to Determine Word Count 04:12

12.8 Knowledge Check

12.9 Assignment 01

12.10 Assignment 01 Demo 02:47

12.11 Assignment 02

12.12 Assignment 02 Demo 03:30

12.13 Quiz

12.14 Key takeaways 01:20

Project 1 18:36

Project 1 Stock Market Data Analysis

Project 1 Demo 18:36

Project 2 20:06

Project 02

Main project 02 20:06

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Course Fee:
USD 599

Self-Study

### Course Status:

Active

1 - 4 hours / week

This course is listed under Open Source , Development & Implementations and Data & Information Management Community

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