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.

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At Simplilearn, we value the trust of our patrons immensely. But, if you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!

- 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

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.

This 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.

### 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

23:58Preview##### 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)

30:31##### 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##### 5.010 Analyse GDP of Countries

##### 5.011 Assignment 01 Demo

03:55##### 5.012 Analyse London Olympics Dataset

##### 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##### 6.008 Solving Linear Algebra problem using SciPy

##### 6.009 Assignment 01 Demo

01:20##### 6.010 Perform CDF and PDF using Scipy

##### 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##### 7.011 Analyse the Federal Aviation Authority Dataset using Pandas

##### 7.012 Assignment 01 Demo

04:09##### 7.013 Analyse NewYork city fire department Dataset

##### 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 One and Two

01:00##### 8.3 Steps Three and Four

##### 8.004 How it Works

01:24##### 8.005 Steps Five and Six

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

00:30##### 8.008 ScikitLearn

02:10##### 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##### 8.15 Knowledge Check

##### 8.016 Analysing Ad Budgets for different media channels

##### 8.017 Assignment One

05:45##### 8.018 Building a model to predict Diabetes

##### 8.019 Assignment Two

05:14##### Knowledge Check

##### 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##### 9.007 Analysing Spam Collection Data

##### 9.008 Demo Assignment 01

06:32##### 9.009 Sentiment Analysis using NLP

##### 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##### 10.007 Draw a pair plot using seaborn library

##### 10.008 Assignment 01 Demo

02:23##### 10.009 Analysing Cause of Death

##### 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##### 11.009 Web Scraping of Simplilearn Website

##### 11.010 Assignment 01 Demo

01:55##### 11.011 Web Scraping of Simplilearn Website Resource page

##### 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

##### 12.009 Determine the wordcount

##### 12.010 Assignment 01 Demo

02:47##### 12.011 Display all the airports based in New York using PySpark

##### 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

23:36##### 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

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

09:37

#### Lesson 10 Hypothesis testing Introduction

12:36##### 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

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

14:42

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

Depending on the learning mode, Simplilearn's Machine Learning Course Completion Certificate can be obtained if:

- The candidate completes one batch of Online Classroom training and finishes one project
- The candidate completes 85% of the Self-Paced Course and finishes one project

### What are the prerequisites for learning Machine Learning?

The prerequisites for this Machine Learning course are:

- Knowledge of basic high school mathematics
- Clarity on the concepts of Python programming
- Fundamental understanding of statistics

Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

### Who provides the certification?

The Machine Learning Certification will be provided by Simplilearn to the candidates who complete the course successfully.

### Is this course accredited?

No, the Machine Learning course offered by Simplilearn is not accredited officially.

### How long does it to take to complete the Machine Learning certification course?

Candidates need to spend 45 - 50 hours of learning for the successful completion of the Machine Learning certification course.

### How long does Simplilearn's certificate for machine learning course valid for?

Simplilearn’s Machine Learning Certification is effective for the lifetime.

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

### Why Learn Machine Learning Certification?

Machine Learning is the technology whose name can be now heard from all the corners of the world. The requirement for proficiency in this promising technology has never been so high.

It is estimated that the market size of machine learning will increase 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 course objectives?

Machine Learning takes Artificial Intelligence to the next level. It has changed the way how people look at data computing and digital transformations. Several innovative applications that will launch in the future are backed by machine learning technology. It is due to the fact that machine learning technology has the ability to process and interpret a large amount of complicated data automatically. Besides, it is cheap as well as fast technology. Machine Learning has found its applications in areas like self-driving cars, facial recognition, recommendation engines, and facial recognition.

According to payscale.com, a Machine Learning Engineer earns an average salary of $134,293 (USD). As a rising demand has been observed for the skilled Machine Learning professionals, candidates can take up this Machine Learning course in Mumbai to get the job competency and hands-on experience with Machine Learning.

### What skills you learn in Machine Learning Certification Training?

The candidates taking the Machine Learning course in Mumbai will be able to do the following:

- Be prepared to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
- Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
- Enhance your knowledge of principles, algorithms, and applications of machine learning through a hands-on approach, which includes working on 28 projects and one Capstone project.
- Learn the concepts of supervised, unsupervised, and reinforcement learning and modeling.
- Explain the machine learning theoretical concepts with its practical aspects.
- Explore the mathematical and heuristic aspects of machine learning.

### Who should take Machine Learning Course?

In all of the major industries, there is a considerable requirement for professionals who are proficient in Machine Learning. This Machine Learning Course is, therefore, ideal for professionals who have an intermediate level of working experience. The following professionals will specifically benefit from the Machine course:

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

### What Machine Learning Real-time Projects you will complete during the course?

We make sure that our Machine Learning course provides coding experience along with hands-on projects. While beginning with the concepts, we also provide theoretical motivation and mathematical problem formulation.

This course includes one primary capstone project and more than 25 ancillary exercises based on 17 machine learning algorithms.

**Capstone Project Details:****Project Name: Predicting house prices in California****Description:**The project involves building a model that predicts median house values in Californian districts. You will be given metrics such as population, median income, median housing price and so on for each block group in California. Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people). The model you build should learn from this data and be able to predict the median housing price in any district.**Concept covered:**Techniques of Machine Learning**Case Study 1: Predict whether the houses will be purchased or not by the consumers, from the given dataset, provided with their salary and age****Project 1:**In reference to the above problem statement, what issues can be observed in the plot generated by the code?**Project 2:**What is the estimated cost of the houses with areas 1700 and 1900?**Concept covered:**Data Preprocessing**Case Study 2: Using the information provided in the dataset, demonstrate the methods to handle missing data, categorical data, and data standardization****Project 3:**Review the training dataset (Excel file). Observe that weight is missing for the fifth and eighth rows. For the mentioned rows, what are the values computed by the imputer?**Project 4:**In the tutorial code, find the call to the Imputer class. Replace the strategy parameter from “mean” to “median” and rerun it. What is the new value assigned to the blank fields Weight and Height for the two rows?**Project 5:**In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?**Case Study 3: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided****Project 6:**What does the hyperplane shadow represent in the PCA output chart on random data?**Project 7:**What is the reconstruction error after PCA transformation? Give interpretation.**Concept Covered:**Regression**Case Study 4: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided****Project 8:**Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and interpret the resulting regression plot. Specify if it is under fitted, right-fitted, or overfitted?**Project 9:**Predict the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.**Project 10:**In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?**Case Study 5: Predict insurance premium per year based on a person’s age using Decision Trees using the information provided in the dataset****Project 11:**Modify the code to predict insurance claim values for people over 55 years of age in the given dataset.**Case Study 6: Generate random quadratic data and demonstrate Decision Tree regression****Project 12:**Modify the max_depth from 2 to 3 or 4, and observe the output.**Project 13:**Modify the max_depth to 20, and observe the output**Project 14:**What is the class prediction for petal_length = 3 cm and petal_width = 1 cm for the max_depth = 2?**Project 15:**Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes exist in the two cases? What does the average value represent these two situations? Use the information provided**Project 16:**Modify the regularization parameter min_sample_leaf from 10 to 6, and check the output of Decision Tree regression. What result do you observe? Explain the reason.**Case Study 7: Use Random Forests to predict insurance per year based on the age of a person.****Project 17:**What is the output insurance value for individuals aged 60 and with n_estimators = 10?**Case Study 8: Demonstrate various regression techniques over a random dataset using the information provided in the dataset****Project 18:**The program shows a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Interpret these charts.**Project 19:**The program shows the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try modifying the values to 0.001, 0.25, and 0.9 and observe the output. Give your interpretation.**Concept Covered:**Classification**Case Study 9: Predict if the houses will be purchased by the consumers, given their salary and age. Use the information provided in the dataset****Project 20:**Typically, the nearest_neighbors for testing class in KNN has the value 5. Modify the code with the value of nearest_neighbours to 2 and 20, and note down your observations.**Case Study 10: Classify IRIS dataset using SVM, and demonstrate how Kernel SVMs can help classify non-linear data.****Project 21:**Modify the kernel trick to linear from RBF to check the type of classifier that is produced for the XOR data in this program. Interpret the data.**Project 22:**For the Iris dataset, add new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Discuss the result.**Case Study 11: Use Decision Trees to classify IRIS flower dataset. Use the information provided.****Project 23:**Run decision tree on the IRIS dataset with max depths of 3 and 4, and display the tree output.**Project 24:**Predict and print class probability for Iris flower instance with petal_len 1 cm and petal_width 0.5 cm.**Case Study 12: Classify the IRIS flower dataset using various classification algorithms. Use the information provided.****Project 25:**Add Logistic Regression classification to the program and compare classification output to previous algorithms?**Concept Covered:**Unsupervised Learning with Clustering**Case Study 13: Demonstrate Clustering algorithm and the Elbow method on a random dataset.****Project 26:**Change the number of clusters k to 2, and record the observations.**Project 27:**Modify the n_samples from 150 to 15000 and the number of centers to 4 with n_clusters as 3. Find the output, and record the observations.**Project 28:**Change the code to set the n_samples from 150 to 15000 and the number of centers to 4, keeping n_clusters at 4. Find the output.**Project 29**: Modify the number of clusters k to 6, and record your observations.### What are the prerequisites for attending Machine Learning Training?

The prerequisites for this Machine Learning course are:

- Knowledge of basic high school mathematics
- Clarity on the concepts of Python programming
- Fundamental understanding of statistics

Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

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