Machine Learning Certification Course
Simplilearn Americas LLC
Course Summary
Simplilearn’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 Description
Who are the trainers?
The training sessions are delivered by highly qualified and certified instructors with relevant industry experience.Can I cancel my enrolment? Do I get a refund?
Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.Are there any group discounts for classroom training programs?
Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide more details.What payment options are available?
Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.Visa Credit or Debit Card
MasterCard
American Express
Diner’s Club
PayPal
I’d like to learn more about this Machine Learning Course. Whom should I contact?
Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.What is Global Teaching Assistance?
Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours for this Big Data Hadoop training course.What is covered under the 24/7 Support promise?
We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us to discuss Big Data and Hadoop topics.What if I miss a class?
Simplilearn has Flexi-pass that lets you attend classes to blend in with your busy schedule and gives you an advantage of being trained by world-class faculty with decades of industry experience combining the best of online classroom training and self-paced learning
With Flexi-pass, Simplilearn gives you access to as many as 15 sessions for 90 days.What is CloudLab?
CloudLab is a cloud-based Python environment lab that Simplilearn offers with the Machine Learning course to ensure a hassle-free execution of your hands-on projects. There is no need to install and maintain Python and it’s libraries on a virtual machine. Instead, you’ll be able to access a pre-configured environment on CloudLab via your browser.
You’ll have access to CloudLab from the Simplilearn LMS (Learning Management System) for the duration of the course.
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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
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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
4.3 Twitter Bootstrap Integration 20:17
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
Module 06 - File Uploading, Ajax and E-mailing 39:28
6.1 What You Will Learn In This Section 00:37
6.2 File Upload 14:04
6.3 Forms 13:19
6.4 Advanced Emailing 10:25
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
7.3 Advanced Map Usage 08:24
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
1.20 Adding Difficulty Levels 09:40
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 Scikit–Learn 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
12.2 Hadoop Core Components
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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