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

18:20Preview##### 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

49:03##### 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

32:43Preview##### 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

25:17##### 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

14:30Preview##### 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

14:27##### 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

23:25##### 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

14:42##### 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.

### Why you should take Machine Learning Certification Course?

- Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning
- The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period

### What are the objectives of this course?

This Machine Learning course in Austin will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.### What will you learn in this Machine Learning Training?

- Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
- Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
- Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
- Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
- Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
- Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

### Who should take this Machine Learning Course in Austin ?

There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

- Developers aspiring to be a data scientist or machine learning engineer
- Analytics managers who are leading a team of analysts
- Business analysts who want to understand data science techniques
- Information architects who want to gain expertise in machine learning algorithms
- Analytics professionals who want to work in machine learning or artificial intelligence
- Graduates looking to build a career in data science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insights

### What projects are included in this Machine Learning Online Training Course?

Simplilearn’s Machine Learning course is a hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms.

**Project 1:**Fare Prediction for Uber**Domain:**Delivery (Commerce)

Uber, one of the largest US-based taxi cab provider, wants to improve the accuracy of fare predicted for any of the trips. Help Uber by building and choosing the right model.**Project 2:**Test bench time reduction for Mercedes-Benz**Domain:**AutomobileMercedes-Benz, a global Germany based automobile manufacturer, wants to reduce the time it spends on the test bench for any car. Faster testing will reduce the time to hit the market. Build and optimize the algorithm by performing dimensionality reduction and various techniques including xgboost to achieve the said objective.

**Project 3:**Income qualification prediction for Inter-American Development bankMany social programs have a hard time making sure the right people are given enough aid. It’s tricky when a program focuses on the poorest segment of the population. This segment of the population can’t provide the necessary income and expense records to prove that they qualify. Predicting the right set of people to be included for the aid remains a big challenge for Inter-American Development Bank. Help the bank by building and improving the accuracy of the model using random forest classifier.

**Project 4:**Access privileges prediction for Amazon.com employeesThere is a considerable amount of data regarding employees’ roles within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. Help Amazon.com to build such a model and suggest the one with maximum accuracy.

### What are the pre-requisites for attending this Machine Learning Course?

Participants in this Machine Learning online course should have:

- Familiarity with the fundamentals of Python programming
- Fair understanding of the basics of statistics and mathematics

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