Machine Learning Course Overview

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.

Machine Learning Certification Key Features

100% Money Back Guarantee
No questions asked refund*

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

Skills Covered

  • 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

Benefits

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 Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture
    Oracle
    Microsoft
    Amazon
    Walmart
    Source: Indeed
  • Annual Salary
    $78KMin
    $114KAverage
    $150KMax
    Source: Glassdoor
    Hiring Companies
    Dell
    Morgan Stanley
    Apple
    Google
    Accenture
    Source: Indeed

Training Options

online Bootcamp

$ 1,499

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online Machine Learning training by top instructors and practitioners
  • Classes starting from:-
3rd Oct: Weekday Class
9th Oct: Weekday Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Machine Learning Course Curriculum

Eligibility

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.
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Pre-requisites

Learners need to possess an undergraduate degree or a high school diploma.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 fundamental courses, including Python for Data Science, Math Refresher, and Statistics Essential for Data Science.
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Course Content

  • Machine Learning

    Preview
    • Lesson 01: Course Introduction

      09:19Preview
      • 1.01 Course Introduction
        06:08
      • 1.02 Demo: Jupyter Lab Walk - Through
        03:11
    • Lesson 02: Introduction to Machine Learning

      08:40Preview
      • 2.01 Learning Objectives
        00:42
      • 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
        02:46
      • 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
        01:23
      • 2.04 Definition and Features of Machine Learning
        01:30
      • 2.05 Machine Learning Approaches
        01:46
      • 2.06 Key Takeaways
        00:33
    • Lesson 03: Supervised Learning Regression and Classification

      02:10:59Preview
      • 3.01 Learning Objectives
        00:46
      • 3.02 Supervised Learning
        02:18
      • 3.03 Supervised Learning: Real Life Scenario
        00:55
      • 3.04 Understanding the Algorithm
        00:54
      • 3.05 Supervised Learning Flow
        01:51
      • 3.06 Types of Supervised Learning: Part A
        01:57
      • 3.07 Types of Supervised Learning: Part B
        02:05
      • 3.08 Types of Classification Algorithms
        01:03
      • 3.09 Types of Regression Algorithms: Part A
        03:23
      • 3.10 Regression Use Case
        00:36
      • 3.11 Accuracy Metrics
        01:24
      • 3.12 Cost Function
        01:49
      • 3.13 Evaluating Coefficients
        00:55
      • 3.14 Demo: Linear Regression
        13:48
      • 3.15 Challenges in Prediction
        01:47
      • 3.16 Types of Regression Algorithms: Part B
        02:40
      • 3.17 Demo: Bigmart
        37:29
      • 3.18 Logistic Regression: Part A
        02:01
      • 3.19 Logistic Regression: Part B
        01:41
      • 3.20 Sigmoid Probability
        02:07
      • 3.21 Accuracy Matrix
        01:28
      • 3.22 Demo: Survival of Titanic Passengers
        13:17
      • 3.23 Overview of Classification
        02:03
      • 3.24 Classification: A Supervised Learning Algorithm
        00:52
      • 3.25 Use Cases
        02:34
      • 3.26 Classification Algorithms
        00:17
      • 3.27 Performance Measures: Confusion Matrix
        02:21
      • 3.28 Performance Measures: Cost Matrix
        02:07
      • 3.29 Naive Bayes Classifier
        01:16
      • 3.30 Steps to Calculate Posterior Probability: Part A
        01:41
      • 3.31 Steps to Calculate Posterior Probability: Part B
        02:22
      • 3.32 Support Vector Machines: Linear Separability
        01:05
      • 3.33 Support Vector Machines: Classification Margin
        02:06
      • 3.34 Linear SVM: Mathematical Representation
        02:05
      • 3.35 Non linear SVMs
        01:07
      • 3.36 The Kernel Trick
        01:19
      • 3.37 Demo: Voice Classification
        10:42
      • 3.38 Key Takeaways
        00:48
    • Lesson 04: Decision Trees and Random Forest

      18:09Preview
      • 4.01 Learning Objectives
        00:37
      • 4.02 Decision Tree: Classifier
        02:17
      • 4.03 Decision Tree: Examples
        01:44
      • 4.04 Decision Tree: Formation
        00:46
      • 4.05 Choosing the Classifier
        02:56
      • 4.06 Overfitting of Decision Trees
        01:01
      • 4.07 Random Forest Classifier Bagging and Bootstrapping
        02:19
      • 4.08 Decision Tree and Random Forest Classifier
        01:07
      • 4.09 Demo: Horse Survival
        04:57
      • 4.10 Key Takeaways
        00:25
    • Lesson 05: Unsupervised Learning

      32:41Preview
      • 5.01 Learning Objectives
        00:36
      • 5.02 Overview
        01:47
      • 5.03 Example and Applications of Unsupervised Learning
        02:17
      • 5.04 Clustering
        01:46
      • 5.05 Hierarchical Clustering
        02:30
      • 5.06 Hierarchical Clustering: Example
        02:02
      • 5.07 Demo: Clustering Animals
        05:40
      • 5.08 K-means Clustering
        03:54
      • 5.09 Optimal Number of Clusters
        03:27
      • 5.10 Demo: Cluster Based Incentivization
        08:18
      • 5.11 Key Takeaways
        00:24
    • Lesson 06: Time Series Modelling

      38:57Preview
      • 6.01 Learning Objectives
        00:24
      • 6.02 Overview of Time Series Modeling
        02:16
      • 6.03 Time Series Pattern Types: Part A
        02:16
      • 6.04 Time Series Pattern Types: Part B
        01:19
      • 6.05 White Noise
        01:06
      • 6.06 Stationarity
        02:13
      • 6.07 Removal of Non Stationarity
        02:13
      • 6.08 Demo: Air Passengers I
        14:26
      • 6.09 Time Series Models: Part A
        02:14
      • 6.10 Time Series Models: Part B
        01:28
      • 6.11 Time Series Models: Part C
        01:51
      • 6.12 Steps in Time Series Forecasting
        00:37
      • 6.13 Demo: Air Passengers II
        06:14
      • 6.14 Key Takeaways
        00:20
    • Lesson 07: Ensemble Learning

      39:35Preview
      • 7.01 Learning Objectives
        00:24
      • 7.02 Overview
        02:41
      • 7.03 Ensemble Learning Methods: Part A
        02:49
      • 7.04 Ensemble Learning Methods: Part B
        04:09
      • 7.05 Working of AdaBoost
        01:43
      • 7.06 AdaBoost Algorithm and Flowchart
        02:28
      • 7.07 Gradient Boosting
        04:37
      • 7.08 XGBoost
        02:23
      • 7.09 XGBoost Parameters: Part A
        03:15
      • 7.10 XGBoost Parameters: Part B
        02:30
      • 7.11 Demo: Pima Indians Diabetes
        03:11
      • 7.12 Model Selection
        02:55
      • 7.13 Common Splitting Strategies
        01:45
      • 7.14 Demo: Cross Validation
        04:18
      • 7.15 Key Takeaways
        00:27
    • Lesson 08: Recommender Systems

      26:11Preview
      • 8.01 Learning Objectives
        00:27
      • 8.02 Introduction
        02:16
      • 8.03 Purposes of Recommender Systems
        00:45
      • 8.04 Paradigms of Recommender Systems
        02:45
      • 8.05 Collaborative Filtering: Part A
        02:14
      • 8.06 Collaborative Filtering: Part B
        01:58
      • 8.07 Association Rule: Mining
        01:47
      • 8.08 Association Rule: Mining Market Basket Analysis
        01:42
      • 8.09 Association Rule: Generation Apriori Algorithm
        00:53
      • 8.10 Apriori Algorithm Example: Part A
        02:13
      • 8.11 Apriori Algorithm Example: Part B
        01:17
      • 8.12 Apriori Algorithm: Rule Selection
        02:52
      • 8.13 Demo: User Movie Recommendation Model
        04:12
      • 8.14 Key Takeaways
        00:50
    • Lesson 09: Level Up Sessions

      10:31
      • Session 01
        05:22
      • Session 02
        05:09
    • Practice Project

      • California Housing Price Prediction
      • Phishing Detector with LR
  • Free Course
  • Math Refresher

    Preview
    • Lesson 01: Course Introduction

      06:23Preview
      • 1.01 About Simplilearn
        00:28
      • 1.02 Introduction to Mathematics
        01:18
      • 1.03 Types of Mathematics
        02:39
      • 1.04 Applications of Math in Data Industry
        01:17
      • 1.05 Learning Path
        00:25
      • 1.06 Course Components
        00:16
    • Lesson 02: Probability and Statistics

      32:38Preview
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check
    • Lesson 03: Coordinate Geometry

      06:31
      • 3.01 Learning Objectives
        00:35
      • 3.02 Introduction to Coordinate Geometry​
        03:16
      • 3.03 Coordinate Geometry Formulas​
        01:51
      • 3.04 Key Takeaways
        00:49
      • 3.05 Knowledge Check
    • Lesson 04: Linear Algebra

      29:53Preview
      • 4.01 Learning Objectives
        00:29
      • 4.02 Introduction to Linear Algebra
        03:21
      • 4.03 Forms of Linear Equation
        05:21
      • 4.04 Solving a Linear Equation
        05:21
      • 4.05 Introduction to Matrices
        02:05
      • 4.06 Matrix Operations
        07:07
      • 4.07 Introduction to Vectors
        01:00
      • 4.08 Types and Properties of Vectors
        01:52
      • 4.09 Vector Operations
        02:39
      • 4.10 Key Takeaways
        00:38
      • 4.11 Knowledge Check
    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      08:56Preview
      • 5.01 Learning Objectives
        00:29
      • 5.02 Eigenvalues
        01:19
      • 5.03 Eigenvectors
        04:09
      • 5.04 Eigendecomposition
        02:21
      • 5.05 Key Takeaways
        00:38
      • 5.06 Knowledge Check
    • Lesson 06: Introduction to Calculus

      09:47Preview
      • 6.01 Learning Objectives
        00:30
      • 6.02 Basics of Calculus
        01:20
      • 6.03 Differential Calculus
        03:01
      • 6.04 Differential Formulas
        01:01
      • 6.05 Integral Calculus
        02:33
      • 6.06 Integration Formulas
        00:47
      • 6.07 Key Takeaways
        00:35
      • 6.08 Knowledge Check
  • 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

      25:49Preview
      • 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
        01:20
      • 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 Applications of Statistics in Business: Time Series Forecasting
        03:50
      • 2.10 Applications of Statistics in Business Sales Forecasting
        03:19
      • 2.11 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

      34:51Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Descriptive Statistics
        02:03
      • 4.03 Mathematical and Positional Averages
        03:15
      • 4.04 Measures of Central Tendancy: Part A
        02:17
      • 4.05 Measures of Central Tendancy: Part B
        02:41
      • 4.06 Measures of Dispersion
        01:15
      • 4.07 Range Outliers Quartiles Deviation
        02:30
      • 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance
        03:37
      • 4.09 Z Score and Empirical Rule
        02:14
      • 4.10 Coefficient of Variation and Its Application
        02:06
      • 4.11 Measures of Shape
        02:39
      • 4.12 Summarizing Data
        02:03
      • 4.13 Recap
        00:54
      • 4.14 Case Study One: Descriptive Statistics
        05:51
    • Lesson 05: Data Visualization

      23:36Preview
      • 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 Data Visualization: Example
        02:41
      • 5.10 Recap
        00:50
      • 5.11 Case Study Two: Data Visualization
        03:04
    • Lesson 06: Probability

      21:51Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Probability Example
        02:02
      • 6.04 Key Terms in Probability
        02:25
      • 6.05 Conditional Probability
        02:11
      • 6.06 Types of Events: Independent and Dependent
        02:59
      • 6.07 Addition Theorem of Probability
        01:58
      • 6.08 Multiplication Theorem of Probability
        02:08
      • 6.09 Bayes Theorem
        03:10
      • 6.10 Recap
        00:53
    • Lesson 07: Probability Distributions

      24:45Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Probability Distribution
        01:25
      • 7.03 Random Variable
        02:21
      • 7.04 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.05 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.06 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.07 Discrete Probability Distributions: Poisson
        03:16
      • 7.08 Binomial by Poisson Theorem
        02:28
      • 7.09 Commonly Used Continuous Probability Distribution
        03:22
      • 7.10 Application of Normal Distribution
        02:49
      • 7.11 Recap
        01:25
    • Lesson 08: Sampling and Sampling Techniques

      36:45Preview
      • 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 Sampling Stratified: Sampling Example
        04:35
      • 8.14 Probability Sampling: Example
        01:17
      • 8.15 Recap
        01:07
      • 8.16 Case Study Three: Sample and Sampling Techniques
        05:16
      • 8.17 Spotlight
        01:42
    • Lesson 09: Inferential Statistics

      37:08Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Inferential Statistics
        03:09
      • 9.03 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.04 Null and Alternate Hypothesis
        01:44
      • 9.05 P Value
        03:22
      • 9.06 Levels of Significance
        01:16
      • 9.07 Type One and Two Errors
        01:37
      • 9.08 Z Test
        02:24
      • 9.09 Confidence Intervals and Percentage Significance Level: Part A
        02:52
      • 9.10 Confidence Intervals: Part B
        01:20
      • 9.11 One Tail and Two Tail Tests
        04:43
      • 9.12 Notes to Remember for Null Hypothesis
        01:02
      • 9.13 Alternate Hypothesis
        01:51
      • 9.14 Recap
        00:56
      • 9.15 Case Study 4: Inferential Statistics
        06:24
      • Hypothesis Testing
    • Lesson 10: Application of Inferential Statistics

      27:20Preview
      • 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
        04:27
      • 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

      20:07Preview
      • 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 Correlation Example
        01:59
      • 11.06 Spearmans Rank Correlation Coefficient
        02:14
      • 11.07 Causation
        01:47
      • 11.08 Example of Regression
        02:28
      • 11.09 Coefficient of Determination
        01:12
      • 11.10 Quantifying Quality
        02:29
      • 11.11 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

      11:47
      • Assisted Practice: Problem Statement
        02:10
      • Assisted Practice: Solution
        09:37

Industry Project

  • Project 1

    Fare Prediction for Uber

    Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies in building its next-generation model.

    Fare Prediction for Uber
  • Project 2

    Test bench time reduction for MercedesBenz

    Mercedes-Benz wants to shorten the time models spend on its test-bench, thus reducing a car’s time to market. Build and optimize a Machine Learning algorithm to solve this problem.

    Test bench time reduction for MercedesBenz
  • Project 3

    Income qualification prediction

    The Inter-American Development bank wants to qualify people for an aid program. Help the bank to build and improve the accuracy of the data set using a random forest classifier.

    Income qualification prediction
  • Project 4

    Access privileges prediction for Amazon employees

    Use the data of Amazon employees and their access permissions to build a model that automatically decides access privileges as employees enter and leave roles within Amazon.

    Access privileges prediction for Amazon employees
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Machine Learning Training Exam & Certification

Machine Learning Certificate
  • Who provides the Machine Learning Course 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 Certificate after training completion which has lifelong validity.

  • How do I become a Machine Learning Engineer?

    This Machine Learning course online 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.

Machine Learning Course Reviews

  • Prabhat K

    Prabhat K

    Product Owner (SAFe) Adaptive Tools

    This course helped me to get promoted with a 30% increment in my salary. In addition, the knowledge I gained enabled me to implement and execute the existing products at Bosch with AI capabilities to win over a significant set of customers.

  • Arjun Nemical

    Arjun Nemical

    Machine Learning Engineer

    The training was awesome. The instructor has done a great job. He was very patient throughout the sessions and took additional time to explain the concepts further when we had queries.

  • Sharath Chenjeri

    Sharath Chenjeri

    My trainer Sonal is amazing and very knowledgeable. The course content is well-planned, comprehensive, and elaborate. Thank you, Simplilearn!

  • Kalpesh Mahajan

    Kalpesh Mahajan

    I like the Simplilearn courses for the following reasons: It provides a unique blend of theoretical and practical based approaches. 2. The learning pace is comfortable. 3. They have global industry experts as trainers.

  • Sharanya Nair

    Sharanya Nair

    Business Analyst

    I had completed Tableau, R, and Python training courses from Simplilearn. These courses helped a lot in moving ahead in my career path. Now, I am pursuing an MS in Data Science. Thank you, Simplilearn!

  • Ashok Kumar Kothandapani

    Ashok Kumar Kothandapani

    Simplilearn’s trainers are patient, clearing any confusion and answering all questions without impacting the course timeline. Simplilearn is the most convenient platform for those who want to grow in the fields of Machine Learning, Data Analytics and Data Science.

  • Jaya Raghavendra

    Jaya Raghavendra

    I am a B.Sc Computers graduate. I had always attended physical classroom sessions, but this is the first time I experienced online classes. Simplilearn allowed learning from different mentors. Big thanks to the support team.

  • Asmita Wankhade

    Asmita Wankhade

    The course content is excellent. You can learn and understand, even if you are only a beginner. I am delighted to have joined and successfully finished the certification. All thanks to Simplilearn.

  • Mahesh Gaonkar

    Mahesh Gaonkar

    Software Engineer

    Simplilearn is a great start for the beginner as well as for the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas on the concepts and exercises. I could finish my course very easily with good project exercises.

  • Kirandeep Kaur

    Kirandeep Kaur

    Simplilearn's service is great. The course instructor Abhilash was very cooperative. The sessions were interactive and exciting. Thank you, Simplilearn.

  • Tapas Bandyopadhyay

    Tapas Bandyopadhyay

    Senior Project Manager

    Simplilearn is the best platform to learn Machine Learning. I have enrolled in this course taught by Vaishali Balaji. Vaishali has excellent knowledge of the subject and covers all machine learning topics - from Linear Regression to XGBoost. The Online Labs are very useful too, for practice.

  • Akila Yukthi

    Akila Yukthi

    I had an incredible learning journey learning Simplilearn's course under Vaishali Balaji. The course was successfully completed on time, and the trainer clarified all our doubts. Simplilearn is one of the best online platforms to learn Machine Learning! Thank you!

  • Parthiban Jayachandran

    Parthiban Jayachandran

    I have enrolled in Simplilearn's Data Science and Advanced Machine Learning programs. The course content is comprehensive and live sessions enriching. Mentors are incredibly knowledgeable, and self-learning videos are helpful. The support team is accommodative and ready to help too.

  • Ganesh N. Jorvekar

    Ganesh N. Jorvekar

    I have enrolled in the PG program in Data Science with Simplilearn, and it has been a fantastic learning experience so far. Simplilearn has an excellent set of trainers who are competent enough to teach the new age technology. Thank you, Simplilearn, for such a great learning journey!

  • Vijay Marupadi

    Vijay Marupadi

    Project Manager at Canadas Best Store Fixtures

    The Simplilearn learning experience was beyond my expectation. The professionalism with which the machine learning training was carried out is worth commending. I would readily recommend Simplilearn to anyone who wants to pursue a career through online learning. It's worth the money. Happy learning with Simplilearn!

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Why Online Bootcamp

  • Develop skills for real career growthCutting-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 trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Machine Learning Certification FAQs

  • 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.

  • Are Machine Learning certifications worth it?

    Having a Machine Learning certification will help you gain the necessary knowledge and training to shape your career in an AI-led future and deal with machine learning problems.

  • What is the career exposure after completing this Machine Learning course?

    Machine learning has gained global traction and many are aspiring to start a career in this field. Jobs in AI and machine learning have grown around 75 percent over the past few years and Gartner predicts that there will be 2.3 million jobs in the field by 2022. Our ML course will give you all the necessary skills to work in this exciting field.

  • What are the job roles available after getting a Machine Learning certification?

    Some of the top job roles in the field of Machine Learning are Data Scientist, Machine Learning Engineer, NLP Scientist, Computer Vision Engineer, and Data Architect. This Machine Learning course gives you all the necessary skills to become eligible for such roles.

  • What does a Machine Learning Engineer do?

    The roles and responsibilities of Machine Learning Engineers include:

    • Designing and building machine learning systems and schemes
    • Analyzing and processing data science prototypes
    • Performing statistical analysis and modifying models using test results
    • Training ML systems whenever required and enhancing prevailing Machine Learning frameworks and libraries
    • Exploring new data to improve the machine’s performance

  • What skills should a Machine Learning Engineer know?

    A Machine Learning Engineer is expected to be skilled in areas like core math, statistics, basic programming, data modeling, neural networks, natural language processing, ML tools and libraries, and more. Our Machine Learning course will impart all of these skills and make you job-ready.

  • What is the difference between Machine Learning and Deep Learning?

    • Machine learning is a subtype of Artificial Intelligence, while deep learning is the evolved version of machine learning.
    • Deep learning is driven by neural networks that imitate neurons in the human brain, embedding a multi-layer architecture. In contrast, machine learning involves the usage of statistical methods to make a machine learn automatically through previously stored data patterns and without the requirement of programming or any human intervention.

  • What is the difference between Machine Learning and Artificial Intelligence?

    Artificial Intelligence is a broad field that encompasses everything that involves giving machines human-like intelligence. Machine learning is an important subset of AI where machines are given a lot of input data and algorithms are applied to train it and give them the ability to ‘learn’ and perform the desired actions. Our ML course deals with this topic in detail.

  • Will this ML course help me to build a successful career in Machine Learning?

    Simplilearn’s Machine Learning certification course is designed by subject matter experts who know what skills are most valued by employers. Topics like types of machine learning, time series modeling, regression, classification, clustering, and deep learning basics are thoroughly covered, and allow you to start a career in this field.

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on execution of Machine Learning projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

  • How is Simplilearn’s Machine Learning course syllabus better than other course providers?

    Simplilearn’s Machine Learning online course is based on a robust syllabus that equips you with extensive knowledge of machine learning concepts and trains you to:

    • Work on real-time data
    • Develop algorithms using both supervised and unsupervised learning methods
    • Create regression, classification, and time series modeling
    • Use Python to draw inferences from different data sets

    Upon completing a lesson, learners are taken through practice sessions to understand concepts better and gain practical knowledge. Additionally, the course offers fundamental courses like ‘Math Refresher’ and ‘Statistical Essential for Data Science’ for those who lack the basic knowledge required to take this course. Hence this is the best course for machine learning which you can opt.

  • Is this will be a live training or pre-recorded videos?

    If you enroll in self-paced e-learning, you will have access to pre-recorded videos. If you enroll in the Online Bootcamp, you will have access to live Machine Learning training conducted online as well as the self-learning content.

  • What if I miss a class?

    Simplilearn provides recordings of each Machine Learning 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 Machine Learning trainers are industry AI 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 the Machine Learning 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.

  • What is online classroom training?

    Online classroom training for the Machine Learning certification is conducted via online live streaming of each class. The classes are conducted by a Machine Learning certified trainer with more than 15 years of work and training experience.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and telephone. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Machine Learning course online with us.

  • How do I enroll in this Machine Learning course?

    You can enroll in this Machine Learning certification course on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • What are the additional benefits I will get after enrolling in Simplilearn’s Machine Learning course?

    Simplilearn’s Machine Learning course offers additional benefits such as:

    • Access to in-depth knowledge of Machine Learning through 58 hours of applied learning, interactive labs, real-life, hands-on projects from Uber, Mercedes Benz, IDB, and 25+ hands-on exercises
    • Constant mentoring and assistance with the coursework from industry experts
    • Flexible training options in the form of self-paced learning, online bootcamp, or corporate training

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administrative fee. To learn more, please read our Refund Policy.

  • Is there any university partnered program in Machine Learning?

    Professionals who take this Machine Learning course do not stop their learning and are inspired to learn more advanced machine learning concepts and seek to understand advanced AI and machine learning concepts as well. Simplilearn’s Post Graduate Program in AI and Machine Learning in partnership with the prestigious Purdue University is ideal for this purpose.

  • * Disclaimer

    * The Machine Learning projects have been built leveraging real publicly available data sets of the mentioned organizations.

  • Is a Machine Learning course difficult to learn?

    Simplilearn’s machine learning course enables you to learn all the machine learning concepts systematically. The course is easy to understand and allows you to align theoretical knowledge with practical knowledge related to Machine learning. This is the best course for machine learning well suited for the ones who have prior knowledge of Statistics, Mathematics, Python programming and want to explore career options in machine learning.

  • Which companies hire Machine Learning Engineers?

    Companies commonly hire engineers with machine learning certifications are Amazon Web Services, Databricks, Dataiku, Google Cloud, IBM, MathWorks, Microsoft Azure, RapidMiner, SAS, and TIBCO.

  • What book do you suggest reading for Machine Learning?

    While taking this machine learning training, you can refer to the following books for a more comprehensive learning experience:

    • Machine Learning Yearning by Andrew Ng
    • Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
    • Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, Michael Munn
    • Hands-on Machine Learning by Aurelien Geron
    • Pattern Recognition & Machine Learning by Christopher M. Bishop

  • What is the pay scale of Machine Learning professionals across the world?

    Professionals with machine learning certification earn an average salary of $113,425 in a year.

  • What are the other courses offered by Simplilearn in Data science and Artificial Intelligence Domain?

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