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

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At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Machine Learning 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!

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Machine Learning 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
- 58 hours of Applied Learning

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

The Machine Learning market is expected to reach USD $30.64 Billion by 2024, at a Compound Annual growth rate(CAGR) of 42.8-percent, indicating the increased adoption of Machine Learning among companies. By 2024, the demand for Machine Learning engineers is expected to grow by 11-percent.

- Designation
- Annual Salary
- Hiring Companies

- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed

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 Machine Learning Engineers or 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. Before getting into the Machine Learning certification training, you should understand these fundamental courses, including Python for Data Science, Math Refresher, and Statistics Essential for Data Science.

### Machine Learning

Preview#### Lesson 01 Course Introduction

08:42Preview##### Course Introduction

05:31##### Accessing Practice Lab

03:11

#### 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

35:57Preview##### 3.1 Learning Objectives

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

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

01:34##### 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

01:36:38Preview##### 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

37:29##### 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##### 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: Level Up Sessions

10:31##### Andrew McAfee | Building Mind-Machine Combinations: Welcome Technology as Your Colleague

05:22##### Andrew McAfee | Building Mind-Machine Combinations: Reduce Error Rates with Human-Computer Teams

05:09

#### Lesson 13 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
### Statistics Essential for Data Science

Preview#### Lesson 01: Course Introduction

07:05Preview##### 1.01 Course Introduction

05:19##### 1.02 What Will You Learn

01:46

#### Lesson 02: Introduction to Statistics

18:41Preview##### 2.01 Learning Objectives

01:16##### 2.02 What Is Statistics

01:50##### 2.03 Why Statistics

02:06##### 2.04 Difference between Population and Sample

01:21##### 2.05 Different Types of Statistics

02:42##### 2.06 Importance of Statistical Concepts in Data Science

03:20##### 2.07 Application of Statistical Concepts in Business

02:11##### 2.08 Case Studies of Statistics Usage in Business

03:09##### 2.09 Recap

00:46

#### Lesson 03: Understanding the Data

17:29Preview##### 3.01 Learning Objectives

01:12##### 3.02 Types of Data in Business Contexts

02:11##### 3.03 Data Categorization and Types of Data

03:13##### 3.03 Types of Data Collection

02:14##### 3.04 Types of Data

02:01##### 3.05 Structured vs. Unstructured Data

01:46##### 3.06 Sources of Data

02:17##### 3.07 Data Quality Issues

01:38##### 3.08 Recap

00:57

#### Lesson 04: Descriptive Statistics

32:03Preview##### 4.01 Learning Objectives

01:26##### 4.02 Mathematical and Positional Averages

03:15##### 4.03 Measures of Central Tendancy: Part A

02:17##### 4.04 Measures of Central Tendancy: Part B

02:41##### 4.05 Measures of Dispersion

01:15##### 4.06 Range Outliers Quartiles Deviation

02:30##### 4.07 Mean Absolute Deviation (MAD) Standard Deviation Variance

02:52##### 4.08 Z Score and Empirical Rule

02:14##### 4.09 Coefficient of Variation and Its Application

02:06##### 4.10 Measures of Shape

02:39##### 4.11 Summarizing Data

02:03##### 4.12 Recap

00:54##### 4.13 Case Study One: Descriptive Statistics

05:51

#### Lesson 05: Data Visualization

20:55Preview##### 5.01 Learning Objectives

00:57##### 5.02 Data Visualization

02:15##### 5.03 Basic Charts

01:52##### 5.04 Advanced Charts

02:19##### 5.05 Interpretation of the Charts

02:57##### 5.06 Selecting the Appropriate Chart

02:25##### 5.07 Charts Do's and Dont's

02:47##### 5.08 Story Telling With Charts

01:29##### 5.09 Recap

00:50##### 5.10 Case Study Two: Data Visualization

03:04

#### Lesson 06: Probability

19:46Preview##### 6.01 Learning Objectives

00:55##### 6.02 Introduction to Probability

03:10##### 6.03 Key Terms in Probability

02:25##### 6.04 Conditional Probability

02:11##### 6.05 Types of Events: Independent and Dependent

02:56##### 6.06 Addition Theorem of Probability

01:58##### 6.07 Multiplication Theorem of Probability

02:08##### 6.08 Bayes Theorem

03:10##### 6.09 Recap

00:53

#### Lesson 07: Probability Distributions

22:29Preview##### 7.01 Learning Objectives

00:52##### 7.02 Random Variable

02:21##### 7.03 Probability Distributions Discrete vs.Continuous: Part A

01:44##### 7.04 Probability Distributions Discrete vs.Continuous: Part B

01:45##### 7.05 Commonly Used Discrete Probability Distributions: Part A

03:18##### 7.06 Discrete Probability Distributions: Poisson

03:16##### 7.07 Binomial by Poisson Theorem

01:37##### 7.08 Commonly Used Continuous Probability Distribution

03:22##### 7.09 Applicaton of Normal Distribution

02:49##### 7.10 Recap

01:25

#### Lesson 08: Sampling and Sampling Techniques

30:53Preview##### 8.01 Learnning Objectives

00:51##### 8.02 Introduction to Sampling and Sampling Errors

03:05##### 8.03 Advantages and Disadvantages of Sampling

01:31##### 8.04 Probability Sampling Methods: Part A

02:32##### 8.05 Probability Sampling Methods: Part B

02:27##### 8.06 Non-Probability Sampling Methods: Part A

01:42##### 8.07 Non-Probability Sampling Methods: Part B

01:25##### 8.08 Uses of Probability Sampling and Non-Probability Sampling

02:08##### 8.09 Sampling

01:08##### 8.10 Probability Distribution

02:53##### 8.11 Theorem Five Point One

00:52##### 8.12 Center Limit Theorem

02:14##### 8.13 Recap

01:07##### 8.14 Case Study Three: Sample and Sampling Techniques

05:16##### 8.15 Spotlight

01:42

#### Lesson 09: Inferential Statistics

33:59Preview##### 9.01 Learning Objectives

01:04##### 9.02 Hypothesis and Hypothesis Testing in Businesses

03:24##### 9.03 Null and Alternate Hypothesis

01:44##### 9.04 P Value

03:22##### 9.05 Levels of Significance

01:16##### 9.06 Type One and Two Errors

01:37##### 9.07 Z Test

02:24##### 9.08 Confidence Intervals and Percentage Significance Level: Part A

02:52##### 9.09 Confidence Intervals: Part B

01:20##### 9.10 One Tail and Two Tail Tests

04:43##### 9.11 Notes to Remember for Null Hypothesis

01:02##### 9.12 Alternate Hypothesis

01:51##### 9.13 Recap

00:56##### 9.14 Case Study 4: Inferential Statistics

06:24##### Hypothesis Testing

#### Lesson 10: Application of Inferential Statistics

27:07Preview##### 10.01 Learning Objectives

00:50##### 10.02 Bivariate Analysis

02:01##### 10.03 Selecting the Appropriate Test for EDA

02:29##### 10.04 Parametric vs. Non-Parametric Tests

01:54##### 10.05 Test of Significance

01:38##### 10.06 Z Test

04:14##### 10.07 T Test

00:54##### 10.08 Parametric Tests ANOVA

03:26##### 10.09 Chi-Square Test

02:31##### 10.10 Sign Test

01:58##### 10.11 Kruskal Wallis Test

01:04##### 10.12 Mann Whitney Wilcoxon Test

01:18##### 10.13 Run Test for Randomness

01:53##### 10.14 Recap

00:57

#### Lesson 11: Relation between Variables

18:08Preview##### 11.01 Learning Objectives

01:06##### 11.02 Correlation

01:54##### 11.03 Karl Pearson's Coefficient of Correlation

02:36##### 11.04 Karl Pearsons: Use Cases

01:30##### 11.05 Spearmans Rank Correlation Coefficient

02:14##### 11.06 Causation

01:47##### 11.07 Example of Regression

02:28##### 11.08 Coefficient of Determination

01:12##### 11.09 Quantifying Quality

02:29##### 11.10 Recap

00:52

#### Lesson 12: Application of Statistics in Business

17:25Preview##### 12.01 Learning Objectives

00:53##### 12.02 How to Use Statistics In Day to Day Business

03:29##### 12.03 Example: How to Not Lie With Statistics

02:34##### 12.04 How to Not Lie With Statistics

01:49##### 12.05 Lying Through Visualizations

02:15##### 12.06 Lying About Relationships

03:31##### 12.07 Recap

01:06##### 12.08 Spotlight

01:48

#### Lesson 13: Assisted Practice

11:47Preview##### Assisted Practice: Problem Statement

02:10##### Assisted Practice: Solution

09:37

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

Upon successful completion of the ML course, Simplilearn will provide you with an industry-recognized Machine Learning Training completion certificate which has lifelong validity.

### How do I become a Machine Learning Engineer?

This Machine Learning course will give you a complete overview of ML methodologies, enough to prepare you to excel in your next role as a Machine Learning Engineer. 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 modeling, and clustering.

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

**Online Classroom:**- Attend one complete batch of Machine Learning training
- 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 Machine Learning course?

Yes, we provide 1 practice test as part of our Machine Learning 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.

**Develop skills for real career growth**Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills**Learn from experts active in their field, not out-of-touch trainers**Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.**Learn by working on real-world problems**Capstone projects involving real world data sets with virtual labs for hands-on learning**Structured guidance ensuring learning never stops**24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

### 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.### What is Machine Learning used for?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

### What are the different types of Machine Learning?

Machine learning is generally divided into three types - Supervised Learning, Unsupervised Learning, and Reinforcement Learning. This

**Machine Learning course**gives you an in-depth understanding of all these three types of machine learning.### Does Machine Learning require coding?

Yes, some coding knowledge is required to perform certain machine learning tasks like statistical analysis. Basic knowledge of either Python, R, or Java is recommended before taking this Machine Learning certification course.

### Is Machine learning a good career?

Machine learning is one of the most in-demand career fields today. Present-day applications like driverless cars, facial recognition, voice assistants, and ecommerce recommendation engines are powered by machine learning. This field will be relevant going forward and professionals entering it can fetch lucrative salaries. As a first step, you can take our machine learning online course and learn everything from scratch.

### How do beginners learn Machine Learning?

Machine learning is in high demand. But before you jump into certification training, it’s essential for beginners to get familiar with the basics of machine learning first. Simplilearn’s free resources articles, tutorials, and YouTube videos will help you get a handle on the concepts and techniques of machine learning. Start your learning with our free ML courses that serve as a foundation for this exciting and dynamic field: Statistics Essentials for Data Science, Math Refresher, and Data Science with Python.

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