This Machine Learning online course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning training course to draw predictions from data.

- Gain expertise with 25+ hands-on exercises
- 4 real-life industry projects with integrated labs
- Dedicated mentoring sessions from industry experts
- 44 hours of instructor-led training with certification

- Supervised and unsupervised learning
- Time series modeling
- Linear and logistic regression
- Kernel SVM
- Decision trees
- K-Means clustering
- Naive Bayes
- Decision tree
- Random forest classifiers
- Boosting and Bagging techniques
- Deep Learning fundamentals

Simplilearn’s Blended Learning model brings classroom learning experience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience.

The Machine Learning certification course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

Read MoreThis Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.

Read More### Machine Learning

Preview#### Lesson 01 Course Introduction

06:41Preview##### Course Introduction

05:31##### Accessing Practice Lab

01:10

#### Lesson 02 Introduction to AI and Machine Learning

19:36Preview##### 2.1 Learning Objectives

00:43##### 2.2 Emergence of Artificial Intelligence

01:56##### 2.3 Artificial Intelligence in Practice

01:48##### 2.4 Sci-Fi Movies with the Concept of AI

00:22##### 2.5 Recommender Systems

00:45##### 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

02:47##### 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

01:23##### 2.8 Definition and Features of Machine Learning

01:30##### 2.9 Machine Learning Approaches

01:48##### 2.10 Machine Learning Techniques

02:21##### 2.11 Applications of Machine Learning: Part A

01:34##### 2.12 Applications of Machine Learning: Part B

02:11##### 2.13 Key Takeaways

00:28##### Knowledge Check

#### Lesson 03 Data Preprocessing

36:19Preview##### 3.1 Learning Objectives

00:38##### 3.2 Data Exploration Loading Files: Part A

02:52##### 3.2 Data Exploration Loading Files: Part B

01:56##### 3.3 Demo: Importing and Storing Data

01:27##### Practice: Automobile Data Exploration - A

##### 3.4 Data Exploration Techniques: Part A

02:56##### 3.5 Data Exploration Techniques: Part B

02:47##### 3.6 Seaborn

02:18##### 3.7 Demo: Correlation Analysis

02:38##### Practice: Automobile Data Exploration - B

##### 3.8 Data Wrangling

01:27##### 3.9 Missing Values in a Dataset

01:55##### 3.10 Outlier Values in a Dataset

01:49##### 3.11 Demo: Outlier and Missing Value Treatment

04:18##### Practice: Data Exploration - C

##### 3.12 Data Manipulation

00:47##### 3.13 Functionalities of Data Object in Python: Part A

01:49##### 3.14 Functionalities of Data Object in Python: Part B

01:33##### 3.15 Different Types of Joins

01:32##### 3.16 Typecasting

01:23##### 3.17 Demo: Labor Hours Comparison

01:54##### Practice: Data Manipulation

##### 3.18 Key Takeaways

00:20##### Knowledge Check

##### Storing Test Results

#### Lesson 04 Supervised Learning

01:21:04Preview##### 4.1 Learning Objectives

00:31##### 4.2 Supervised Learning

02:17##### 4.3 Supervised Learning- Real-Life Scenario

00:53##### 4.4 Understanding the Algorithm

00:52##### 4.5 Supervised Learning Flow

01:50##### 4.6 Types of Supervised Learning: Part A

01:54##### 4.7 Types of Supervised Learning: Part B

02:03##### 4.8 Types of Classification Algorithms

01:01##### 4.9 Types of Regression Algorithms: Part A

03:20##### 4.10 Regression Use Case

00:34##### 4.11 Accuracy Metrics

01:23##### 4.12 Cost Function

01:48##### 4.13 Evaluating Coefficients

00:53##### 4.14 Demo: Linear Regression

13:47##### Practice: Boston Homes - A

##### 4.15 Challenges in Prediction

01:45##### 4.16 Types of Regression Algorithms: Part B

02:40##### 4.17 Demo: Bigmart

21:55##### Practice: Boston Homes - B

##### 4.18 Logistic Regression: Part A

01:58##### 4.19 Logistic Regression: Part B

01:38##### 4.20 Sigmoid Probability

02:05##### 4.21 Accuracy Matrix

01:36##### 4.22 Demo: Survival of Titanic Passengers

14:07##### Practice: Iris Species

##### 4.23 Key Takeaways

00:14##### Knowledge Check

##### Health Insurance Cost

#### Lesson 05 Feature Engineering

27:52Preview##### 5.1 Learning Objectives

00:27##### 5.2 Feature Selection

01:28##### 5.3 Regression

00:53##### 5.4 Factor Analysis

01:57##### 5.5 Factor Analysis Process

01:05##### 5.6 Principal Component Analysis (PCA)

02:31##### 5.7 First Principal Component

02:43##### 5.8 Eigenvalues and PCA

02:32##### 5.9 Demo: Feature Reduction

05:47##### Practice: PCA Transformation

##### 5.10 Linear Discriminant Analysis

02:27##### 5.11 Maximum Separable Line

00:44##### 5.12 Find Maximum Separable Line

03:12##### 5.13 Demo: Labeled Feature Reduction

01:53##### Practice: LDA Transformation

##### 5.14 Key Takeaways

00:13##### Knowledge Check

##### Simplifying Cancer Treatment

#### Lesson 06 Supervised Learning Classification

55:43Preview##### 6.1 Learning Objectives

00:34##### 6.2 Overview of Classification

02:05##### Classification: A Supervised Learning Algorithm

00:52##### 6.4 Use Cases of Classification

02:37##### 6.5 Classification Algorithms

00:16##### 6.6 Decision Tree Classifier

02:17##### 6.7 Decision Tree Examples

01:45##### 6.8 Decision Tree Formation

00:47##### 6.9 Choosing the Classifier

02:55##### 6.10 Overfitting of Decision Trees

01:00##### 6.11 Random Forest Classifier- Bagging and Bootstrapping

02:22##### 6.12 Decision Tree and Random Forest Classifier

01:06##### Performance Measures: Confusion Matrix

02:21##### Performance Measures: Cost Matrix

02:06##### 6.15 Demo: Horse Survival

08:30##### Practice: Loan Risk Analysis

##### 6.16 Naive Bayes Classifier

01:28##### 6.17 Steps to Calculate Posterior Probability: Part A

01:44##### 6.18 Steps to Calculate Posterior Probability: Part B

02:21##### 6.19 Support Vector Machines : Linear Separability

01:05##### 6.20 Support Vector Machines : Classification Margin

02:05##### 6.21 Linear SVM : Mathematical Representation

02:04##### 6.22 Non-linear SVMs

01:06##### 6.23 The Kernel Trick

01:19##### 6.24 Demo: Voice Classification

10:42##### Practice: College Classification

##### 6.25 Key Takeaways

00:16##### Knowledge Check

##### Classify Kinematic Data

#### Lesson 07 Unsupervised Learning

28:26Preview##### 7.1 Learning Objectives

00:29##### 7.2 Overview

01:48##### 7.3 Example and Applications of Unsupervised Learning

02:17##### 7.4 Clustering

01:49##### 7.5 Hierarchical Clustering

02:28##### 7.6 Hierarchical Clustering Example

02:01##### 7.7 Demo: Clustering Animals

05:39##### Practice: Customer Segmentation

##### 7.8 K-means Clustering

01:46##### 7.9 Optimal Number of Clusters

01:24##### 7.10 Demo: Cluster Based Incentivization

08:32##### Practice: Image Segmentation

##### 7.11 Key Takeaways

00:13##### Knowledge Check

##### Clustering Image Data

#### Lesson 08 Time Series Modeling

37:44Preview##### 8.1 Learning Objectives

00:24##### 8.2 Overview of Time Series Modeling

02:16##### 8.3 Time Series Pattern Types: Part A

02:16##### 8.4 Time Series Pattern Types: Part B

01:19##### 8.5 White Noise

01:07##### 8.6 Stationarity

02:13##### 8.7 Removal of Non-Stationarity

02:13##### 8.8 Demo: Air Passengers - A

14:33##### Practice: Beer Production - A

##### 8.9 Time Series Models: Part A

02:14##### 8.10 Time Series Models: Part B

01:28##### 8.11 Time Series Models: Part C

01:51##### 8.12 Steps in Time Series Forecasting

00:37##### 8.13 Demo: Air Passengers - B

05:01##### Practice: Beer Production - B

##### 8.14 Key Takeaways

00:12##### Knowledge Check

##### IMF Commodity Price Forecast

#### Lesson 09 Ensemble Learning

35:41Preview##### 9.01 Ensemble Learning

00:24##### 9.2 Overview

02:41##### 9.3 Ensemble Learning Methods: Part A

02:28##### 9.4 Ensemble Learning Methods: Part B

02:37##### 9.5 Working of AdaBoost

01:43##### 9.6 AdaBoost Algorithm and Flowchart

02:28##### 9.7 Gradient Boosting

02:36##### 9.8 XGBoost

02:23##### 9.9 XGBoost Parameters: Part A

03:15##### 9.10 XGBoost Parameters: Part B

02:30##### 9.11 Demo: Pima Indians Diabetes

04:14##### Practice: Linearly Separable Species

##### 9.12 Model Selection

02:08##### 9.13 Common Splitting Strategies

01:45##### 9.14 Demo: Cross Validation

04:18##### Practice: Model Selection

##### 9.15 Key Takeaways

00:11##### Knowledge Check

##### Tuning Classifier Model with XGBoost

#### Lesson 10 Recommender Systems

25:45Preview##### 10.1 Learning Objectives

00:28##### 10.2 Introduction

02:17##### 10.3 Purposes of Recommender Systems

00:45##### 10.4 Paradigms of Recommender Systems

02:45##### 10.5 Collaborative Filtering: Part A

02:14##### 10.6 Collaborative Filtering: Part B

01:58##### 10.7 Association Rule Mining

01:47##### Association Rule Mining: Market Basket Analysis

01:43##### 10.9 Association Rule Generation: Apriori Algorithm

00:53##### 10.10 Apriori Algorithm Example: Part A

02:11##### 10.11 Apriori Algorithm Example: Part B

01:18##### 10.12 Apriori Algorithm: Rule Selection

02:52##### 10.13 Demo: User-Movie Recommendation Model

04:19##### Practice: Movie-Movie recommendation

##### 10.14 Key Takeaways

00:15##### Knowledge Check

##### Book Rental Recommendation

#### Lesson 11 Text Mining

43:58Preview##### 11.1 Learning Objectives

00:22##### 11.2 Overview of Text Mining

02:11##### 11.3 Significance of Text Mining

01:26##### 11.4 Applications of Text Mining

02:23##### 11.5 Natural Language ToolKit Library

02:35##### 11.6 Text Extraction and Preprocessing: Tokenization

00:33##### 11.7 Text Extraction and Preprocessing: N-grams

00:55##### 11.8 Text Extraction and Preprocessing: Stop Word Removal

01:24##### 11.9 Text Extraction and Preprocessing: Stemming

00:44##### 11.10 Text Extraction and Preprocessing: Lemmatization

00:35##### 11.11 Text Extraction and Preprocessing: POS Tagging

01:17##### 11.12 Text Extraction and Preprocessing: Named Entity Recognition

00:54##### 11.13 NLP Process Workflow

00:53##### 11.14 Demo: Processing Brown Corpus

10:05##### Practice: Wiki Corpus

##### 11.15 Structuring Sentences: Syntax

01:54##### 11.16 Rendering Syntax Trees

00:55##### 11.17 Structuring Sentences: Chunking and Chunk Parsing

01:38##### 11.18 NP and VP Chunk and Parser

01:39##### 11.19 Structuring Sentences: Chinking

01:44##### 11.20 Context-Free Grammar (CFG)

01:56##### 11.21 Demo: Structuring Sentences

07:46##### Practice: Airline Sentiment

##### 11.22 Key Takeaways

00:09##### Knowledge Check

##### FIFA World Cup

#### Lesson 12 Project Highlights

02:40##### Project Highlights

02:40##### Uber Fare Prediction

##### Amazon - Employee Access

#### Practice Projects

##### California Housing Price Prediction

##### Phishing Detector with LR

- Free Course
### Data Science with Python

Preview#### Lesson 00 - Course Overview

04:34Preview##### 0.001 Course Overview

04:34

#### Lesson 01 - Data Science Overview

20:27Preview##### 1.001 Introduction to Data Science

08:42##### 1.002 Different Sectors Using Data Science

05:59##### 1.003 Purpose and Components of Python

05:02##### 1.4 Quiz

##### 1.005 Key Takeaways

00:44

#### Lesson 02 - Data Analytics Overview

18:20##### 2.001 Data Analytics Process

07:21##### 2.2 Knowledge Check

##### 2.3 Exploratory Data Analysis(EDA)

##### 2.4 EDA-Quantitative Technique

##### 2.005 EDA - Graphical Technique

00:57##### 2.006 Data Analytics Conclusion or Predictions

04:30##### 2.007 Data Analytics Communication

02:06##### 2.8 Data Types for Plotting

##### 2.009 Data Types and Plotting

02:29##### 2.11 Quiz

##### 2.012 Key Takeaways

00:57##### 2.10 Knowledge Check

#### Lesson 03 - Statistical Analysis and Business Applications

23:53Preview##### 3.001 Introduction to Statistics

01:31##### 3.2 Statistical and Non-statistical Analysis

##### 3.003 Major Categories of Statistics

01:34##### 3.4 Statistical Analysis Considerations

##### 3.005 Population and Sample

02:15##### 3.6 Statistical Analysis Process

##### 3.007 Data Distribution

01:48##### 3.8 Dispersion

##### 3.9 Knowledge Check

##### 3.010 Histogram

03:59##### 3.11 Knowledge Check

##### 3.012 Testing

08:18##### 3.13 Knowledge Check

##### 3.014 Correlation and Inferential Statistics

02:57##### 3.15 Quiz

##### 3.016 Key Takeaways

01:31

#### Lesson 04 - Python Environment Setup and Essentials

23:58##### 4.001 Anaconda

02:54##### 4.2 Installation of Anaconda Python Distribution (contd.)

##### 4.003 Data Types with Python

13:28##### 4.004 Basic Operators and Functions

06:26##### 4.5 Quiz

##### 4.006 Key Takeaways

01:10

#### Lesson 05 - Mathematical Computing with Python (NumPy)

30:31Preview##### 5.001 Introduction to Numpy

05:30##### 5.2 Activity-Sequence it Right

##### 5.003 Demo 01-Creating and Printing an ndarray

04:50##### 5.4 Knowledge Check

##### 5.5 Class and Attributes of ndarray

##### 5.006 Basic Operations

07:04##### 5.7 Activity-Slice It

##### 5.8 Copy and Views

##### 5.009 Mathematical Functions of Numpy

05:01##### 5.10 Assignment 01

##### 5.011 Assignment 01 Demo

03:55##### 5.12 Assignment 02

##### 5.013 Assignment 02 Demo

03:16##### 5.14 Quiz

##### 5.015 Key Takeaways

00:55

#### Lesson 06 - Scientific computing with Python (Scipy)

23:35Preview##### 6.001 Introduction to SciPy

06:57##### 6.002 SciPy Sub Package - Integration and Optimization

05:51##### 6.3 Knowledge Check

##### 6.4 SciPy sub package

##### 6.005 Demo - Calculate Eigenvalues and Eigenvector

01:36##### 6.6 Knowledge Check

##### 6.007 SciPy Sub Package - Statistics, Weave and IO

05:46##### 6.8 Assignment 01

##### 6.009 Assignment 01 Demo

01:20##### 6.10 Assignment 02

##### 6.011 Assignment 02 Demo

00:55##### 6.12 Quiz

##### 6.013 Key Takeaways

01:10

#### Lesson 07 - Data Manipulation with Pandas

47:34Preview##### 7.001 Introduction to Pandas

12:29##### 7.2 Knowledge Check

##### 7.003 Understanding DataFrame

05:31##### 7.004 View and Select Data Demo

05:34##### 7.005 Missing Values

03:16##### 7.006 Data Operations

09:56##### 7.7 Knowledge Check

##### 7.008 File Read and Write Support

00:31##### 7.9 Knowledge Check-Sequence it Right

##### 7.010 Pandas Sql Operation

02:00##### 7.11 Assignment 01

##### 7.012 Assignment 01 Demo

04:09##### 7.13 Assignment 02

##### 7.014 Assignment 02 Demo

02:34##### 7.15 Quiz

##### 7.016 Key Takeaways

01:34

#### Lesson 08 - Machine Learning with Scikit–Learn

01:02:10Preview##### 8.001 Machine Learning Approach

03:57##### 8.002 Steps 1 and 2

01:00##### 8.3 Steps 3 and 4

##### 8.004 How it Works

01:24##### 8.005 Steps 5 and 6

01:54##### 8.006 Supervised Learning Model Considerations

00:30##### 8.7 Knowledge Check

##### 8.008 Scikit-Learn

02:10##### 8.9 Knowledge Check

##### 8.010 Supervised Learning Models - Linear Regression

11:19##### 8.011 Supervised Learning Models - Logistic Regression

08:43##### 8.012 Unsupervised Learning Models

10:40##### 8.013 Pipeline

02:37##### 8.014 Model Persistence and Evaluation

05:45##### 8.16 Assignment 01

##### 8.15 Knowledge Check

##### 8.017 Assignment 01

05:45##### 8.18 Assignment 02

##### 8.019 Assignment 02

05:14##### 8.20 Quiz

##### 8.021 Key Takeaways

01:12

#### Lesson 09 - Natural Language Processing with Scikit Learn

49:03Preview##### 9.001 NLP Overview

10:42##### 9.2 NLP Applications

##### 9.3 Knowledge Check

##### 9.004 NLP Libraries-Scikit

12:29##### 9.5 Extraction Considerations

##### 9.006 Scikit Learn-Model Training and Grid Search

10:17##### 9.7 Assignment 01

##### 9.008 Demo Assignment 01

06:32##### 9.9 Assignment 02

##### 9.010 Demo Assignment 02

08:00##### 9.11 Quiz

##### 9.012 Key Takeaway

01:03

#### Lesson 10 - Data Visualization in Python using matplotlib

32:43##### 10.001 Introduction to Data Visualization

08:01##### 10.2 Knowledge Check

##### 10.3 Line Properties

##### 10.004 (x,y) Plot and Subplots

10:01##### 10.5 Knowledge Check

##### 10.006 Types of Plots

09:32##### 10.7 Assignment 01

##### 10.008 Assignment 01 Demo

02:23##### 10.9 Assignment 02

##### 10.010 Assignment 02 Demo

01:47##### 10.11 Quiz

##### 10.012 Key Takeaways

00:59

#### Lesson 11 - Web Scraping with BeautifulSoup

52:26Preview##### 11.001 Web Scraping and Parsing

12:50##### 11.2 Knowledge Check

##### 11.003 Understanding and Searching the Tree

12:56##### 11.4 Navigating options

##### 11.005 Demo3 Navigating a Tree

04:22##### 11.6 Knowledge Check

##### 11.007 Modifying the Tree

05:37##### 11.008 Parsing and Printing the Document

09:05##### 11.9 Assignment 01

##### 11.010 Assignment 01 Demo

01:55##### 11.11 Assignment 02

##### 11.012 Assignment 02 demo

04:57##### 11.13 Quiz

##### 11.014 Key takeaways

00:44

#### Lesson 12 - Python integration with Hadoop MapReduce and Spark

40:39Preview##### 12.001 Why Big Data Solutions are Provided for Python

04:55##### 12.2 Hadoop Core Components

##### 12.003 Python Integration with HDFS using Hadoop Streaming

07:20##### 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count

08:52##### 12.5 Knowledge Check

##### 12.006 Python Integration with Spark using PySpark

07:43##### 12.007 Demo 02 - Using PySpark to Determine Word Count

04:12##### 12.8 Knowledge Check

##### 12.9 Assignment 01

##### 12.010 Assignment 01 Demo

02:47##### 12.11 Assignment 02

##### 12.012 Assignment 02 Demo

03:30##### 12.13 Quiz

##### 12.014 Key takeaways

01:20

#### Practice Projects

##### IBM HR Analytics Employee Attrition Modeling.

- Free Course
### Statistics Essential for Data Science

Preview#### Lesson 1 Introduction

02:55Preview##### 1.1 Introduction

02:55

#### Lesson 2 Sample or population data

03:56Preview##### 2.1 Sample or population data

03:56

#### Lesson 3 The fundamentals of descriptive statistics

21:18Preview##### 3.1 The fundamentals of descriptive statistics

03:18##### 3.2 Levels of measurement

02:57##### 3.3 Categorical variables. Visualization techniques for categorical variables

04:06##### 3.4 Numerical variables. Using a frequency distribution table

03:24##### 3.5 Histogram charts

02:27##### 3.6 Cross tables and scatter plots

05:06

#### Lesson 4 Measures of central tendency, asymmetry, and variability

25:17Preview##### 4.1 Measures of central tendency, asymmetry, and variability

04:24##### 4.2 Measuring skewness

02:43##### 4.3 Measuring how data is spread out calculating variance

05:58##### 4.4 Standard deviation and coefficient of variation

04:54##### 4.5 Calculating and understanding covariance

03:31##### 4.6 The correlation coefficient

03:47

#### Lesson 5 Practical example descriptive statistics

14:30##### 5.1 Practical example descriptive statistics

14:30

#### Lesson 6 Distributions

16:17Preview##### 6.1 Distributions

01:02##### 6.2 What is a distribution

03:40##### 6.3 The Normal distribution

03:45##### 6.4 The standard normal distribution

02:51##### 6.5 Understanding the central limit theorem

03:40##### 6.6 Standard error

01:19

#### Lesson 7 Estimators and Estimates

23:36Preview##### 7.1 Estimators and Estimates

02:36##### 7.2 Confidence intervals - an invaluable tool for decision making

06:31##### 7.3 Calculating confidence intervals within a population with a known variance

02:30##### 7.4 Student’s T distribution

03:14##### 7.5 Calculating confidence intervals within a population with an unknown variance

04:07##### 7.6 What is a margin of error and why is it important in Statistics

04:38

#### Lesson 8 Confidence intervals advanced topics

14:27Preview##### 8.1 Confidence intervals advanced topics

04:47##### 8.2 Calculating confidence intervals for two means with independent samples (part One)

04:36##### 8.3 Calculating confidence intervals for two means with independent samples (part two)

03:40##### 8.4 Calculating confidence intervals for two means with independent samples (part three)

01:24

#### Lesson 9 Practical example inferential statistics

09:37##### 9.1 Practical example inferential statistics

09:37

#### Lesson 10 Hypothesis testing Introduction

12:36Preview##### 10.1 Hypothesis testing Introduction

04:56##### 10.2 Establishing a rejection region and a significance level

04:20##### 10.3 Type I error vs Type II error

03:20

#### Lesson 11 Hypothesis testing Let's start testing!

26:39Preview##### 11.1 Hypothesis testing Let's start testing!

06:07##### 11.2 What is the p-value and why is it one of the most useful tool for statisticians

03:55##### 11.3 Test for the mean. Population variance unknown

04:26##### 11.4 Test for the mean. Dependent samples

04:45##### 11.5 Test for the mean. Independent samples (Part One)

03:38##### 11.6 Test for the mean. Independent samples (Part Two)

03:48

#### Lesson 12 Practical example hypothesis testing

06:31##### 12.1 Practical example hypothesis testing

06:31

#### Lesson 13 The fundamentals of regression analysis

18:32Preview##### 13.1 The fundamentals of regression analysis

01:02##### 13.2 Correlation and causation

04:06##### 13.3 The linear regression model made easy

05:02##### 13.4 What is the difference between correlation and regression

01:28##### 13.5 A geometrical representation of the linear regression model

01:18##### 13.6 A practical example - Reinforced learning

05:36

#### Lesson 14 Subtleties of regression analysis

23:25Preview##### 14.1 Subtleties of regression analysis

02:04##### 14.2 What is Rsquared and how does it help us

05:00##### 14.3 The ordinary least squares setting and its practical applications

02:08##### 14.4 Studying regression tables

04:34##### 14.5 The multiple linear regression model

02:42##### 14.6 Adjusted R-squared

04:57##### 14.7 What does the F-statistic show us and why we need to understand it

02:00

#### Lesson 15 Assumptions for linear regression analysis

19:16Preview##### 15.1 Assumptions for linear regression analysis

02:11##### 15.2 Linearity

01:40##### 15.3 No endogeneity

03:43##### 15.4 Normality and homoscedasticity

05:09##### 15.5 No autocorrelation

03:11##### 15.6 No multicollinearity

03:22

#### Lesson 16 Dealing with categorical data

05:20##### 16.1 Dealing with categorical data

05:20

#### Lesson 17 Practical example regression analysis

14:42Preview##### 17.1 Practical example regression analysis

14:42

### Who provides the certificate and how long is it valid for?

Upon successful completion of this course, Simplilearn will provide you with an industry-recognized course completion certificate which has a lifelong validity.### How do I become a Machine Learning expert?

This course will give you a complete overview of Machine Learning methodologies, enough to prepare you to excel in your next role as a Machine Learning expert. You will earn Simplilearn’s Machine Learning certification that will attest to your new skills and on-the-job expertise. Get familiar with regression, classification, time series modelling, and clustering.

### What do I need to do to unlock my Simplilearn certificate?

**Online Classroom:**- Attend one complete batch
- Submit at least one completed project.

**Online Self-Learning:**- Complete 85% of the course
- Submit at least one completed project.

### Do you provide any practice tests as part of this course?

Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Machine Learning Multiple Choice Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.

### What is Machine Learning?

Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.

### How will the labs be conducted?

Simplilearn provides Integrated labs for all the hands-on execution of projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.### Is this live training, or will I watch pre-recorded videos?

If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the online classroom Flexi Pass, you will have access to live training conducted online as well as the pre-recorded videos.### What if I miss a class?

Simplilearn provides recordings of each class so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.### Who are the instructors and how are they selected?

All of our highly qualified trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.### 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.

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.