Machine Learning using Python

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Machine Learning Course Course Overview

This ML 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. In this machine learning certification training, you will learn how to use Python to draw predictions from data.

Machine Learning Training 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!
  • 30+ hours of blended learning
  • 30+ assisted practices and lesson-wise knowledge checks
  • Lifetime access to self-paced learning content
  • Industry-based projects for experiential learning
  • Interactive learning with Google Colabs
  • Dedicated live sessions by faculty of industry experts
  • 30+ hours of blended learning
  • Industry-based projects for experiential learning
  • 30+ assisted practices and lesson-wise knowledge checks
  • Interactive learning with Google Colabs
  • Lifetime access to self-paced learning content
  • Dedicated live sessions by faculty of industry experts
  • 30+ hours of blended learning
  • Industry-based projects for experiential learning
  • 30+ assisted practices and lesson-wise knowledge checks
  • Interactive learning with Google Colabs
  • Lifetime access to self-paced learning content
  • Dedicated live sessions by faculty of industry experts

Skills Covered

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

Begin your journey to success

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Benefits

The Machine Learning market is expected to reach USD 419.94 Billion by 2030 at a Compound Annual Growth Rate(CAGR) of 34.8%, indicating the increased adoption of machine learning among companies. 

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    $71KMin
    $110KAverage
    $200KMax
    Source: Glassdoor
    Hiring Companies
    Source: Indeed
  • Annual Salary
    $67KMin
    $105KAverage
    $205KMax
    Source: Glassdoor
    Hiring Companies
    Source: Indeed

Training Options

online Bootcamp

  • Flexi Pass Enabled: Flexibility to reschedule your cohort within first 90 days of access.
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Batch starting from:
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20th May, Weekday Class
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27% Off$499$684

Corporate Training

Customised to enterprise 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 using Python Course Curriculum

Eligibility

The Machine Learning certification course is well-suited for intermediate-level participants, 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, one should understand fundamental courses, including Python for data science, math refreshers, and statistics essential for data science.
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Course Content

  • Machine Learning using Python

    Preview
    • Lesson 01 - Course Introduction

      04:34Preview
      • 1.01 Course Introduction
        02:39
      • 1.02 What You Will Learn
        01:55
    • Lesson 02 - Introduction to Machine Learning

      14:30Preview
      • 2.01 Introduction
        00:51
      • 2.02 What Is Machine Learning?
        02:49
      • 2.03 Types of Machine Learning
        02:54
      • 2.04 Machine Learning Pipeline and MLOP's
        03:35
      • 2.05 Introduction to Python Packages Used in Machine Learning
        03:29
      • 2.06 Recap
        00:52
    • Lesson 03 - Supervised Learning

      26:34Preview
      • 3.01 Introduction
        00:41
      • 3.02 Supervised Learning
        02:34
      • 3.03 Applications of Supervised Learning
        03:11
      • 3.04 Preparing and Shaping Data
        06:50
      • 3.05 What is overfitting and underfitting?
        02:23
      • 3.06 Detecting and Preventing Overfitting and Underfitting
        07:36
      • 3.07 Regularization
        02:38
      • 3.08 Recap
        00:41
    • Lesson 04 - Regression and Applications

      01:30:37Preview
      • 4.01 Introduction
        01:14
      • 4.02 What is Regression?
        01:34
      • 4.03 Regression Types: Introduction
        02:45
      • 4.04 Linear Regression
        02:48
      • 4.05 Working with Linear Regression
        35:14
      • 4.06 Critical Assumptions for Linear Regression
        01:31
      • 4.07 Logistic Regression
        02:33
      • 4.08 Data Exploration Using SMOTE
        12:56
      • 4.09 Over Sampling Using SMOTE
        01:48
      • 4.10 Polynomial Regression
        02:41
      • 4.11 Data Preparation Model Building and Performance Evaluation Part A
        04:53
      • 4.12 Ridge Regression
        01:57
      • 4.13 Data Preparation Model Building: Part B
        06:25
      • 4.14 LASSO Regression
        02:30
      • 4.15 Data Preparation Model Building: Part C
        06:13
      • 4.16 Recap
        00:55
      • 4.17 Spotlight
        02:40
    • Lesson 05 - Classification and Applications

      01:06:44Preview
      • 5.01 Introduction
        01:03
      • 5.02 What are Classification Algorithms?
        02:09
      • 5.03 Types of Classification
        03:29
      • 5.04 Types and selection of performance parameters
        04:58
      • 5.05 Naive Bayes
        02:56
      • 5.06 Applying Naive Bayes Classifier
        03:27
      • 5.07 Stochastic Gradient Descent
        03:25
      • 5.08 Applying Stochastic Gradient Descent
        05:02
      • 5.09 K Nearest Neighbors
        02:41
      • 5.10 Applying K Nearest Neighbors
        05:28
      • 5.11 Decision Tree
        02:42
      • 5.12 Applying Decision Tree
        04:27
      • 5.13 Random Forest
        01:59
      • 5.14 Applying Random Forest
        03:22
      • 5.15 Boruta Explained
        01:15
      • 5.16 Automatic Feature Selection with Boruta
        06:43
      • 5.17 Support Vector Machine
        02:27
      • 5.18 Applying Support Vector Machine
        07:07
      • 5.19 Cohens Kappa Measure
        01:22
      • 5.20 Recap
        00:42
    • Lesson 06 - Unsupervised Algorithms

      01:15:00Preview
      • 6.01 Introduction
        00:53
      • 6.02 What are Unsupervised Algorithms?
        02:51
      • 6.03 Types of Unsupervised Algorithms Clustering and Associative
        02:15
      • 6.04 When to Use Unsupervised Algorithms?
        01:22
      • 6.05 Visualizing Outputs
        06:14
      • 6.06 Performance Parameters
        02:55
      • 6.07 Clustering Types
        00:56
      • 6.08 Hierarchical Clustering
        03:32
      • 6.09 Applying Hierarchical Clustering
        03:22
      • 6.10 K means Clustering: Part 1
        02:30
      • 6.11 K means Clustering: Part 2
        01:54
      • 6.12 Applying K Means Clustering
        03:37
      • 6.13 KNN-K Nearest Neighbors
        03:41
      • 6.14 Outlier Detection
        01:47
      • 6.15 Outlier Detection Algorithms in PyOD
        02:49
      • 6.16 Demo: K NN for Anomaly Detection
        02:37
      • 6.17 Principal Component Analysis
        04:15
      • 6.18 Applying Principal Component Analysis: PCA
        04:21
      • 6.19 Correspondence Analysis Multiple correspondence analysis: MCA
        03:16
      • 6.20 Singular Value Decomposition
        02:06
      • 6.21 Applying Singular Value Decomposition
        04:14
      • 6.22 Independent Component Analysis
        02:26
      • 6.23 Applying Independent Component Analysis
        01:54
      • 6.24 BIRCH
        02:33
      • 6.25 Applying BIRCH
        02:15
      • 6.26 Recap
        01:05
      • 6.27 Spotlight
        03:20
    • Lesson 07 - Ensemble Learning

      59:58Preview
      • 7.01 Introduction
        00:54
      • 7.02 What is Ensemble Learning?
        01:46
      • 7.03 Categories in Ensemble Learning
        02:47
      • 7.04 Sequential Ensemble Technique
        02:50
      • 7.05 Parallel Ensemble Technique
        02:10
      • 7.06 Types of Ensemble Methods
        01:56
      • 7.07 Bagging
        03:01
      • 7.08 Demo: Bagging
        02:53
      • 7.09 Boosting
        02:14
      • 7.10 Demo: Boosting
        03:29
      • 7.11 Stacking
        02:56
      • 7.12 Demo: Stacking
        03:44
      • 7.13 Reducing Errors with Ensembles
        05:27
      • 7.14 Applying Averaging and Max Voting
        06:33
      • 7.15 Hello World Tensorflow
        02:38
      • 7.16 Hands on with TensorFlow: Part A
        05:09
      • 7.17 Keras
        02:49
      • 7.18 Hands on with TensorFlow: Part B
        05:57
      • 7.19 Recap
        00:45
    • Lesson 08 - Recommender System

      58:24Preview
      • 8.01 Introduction
        01:00
      • 8.02 How do recommendation engines work
        02:45
      • 8.03 Recommendation Engine: Use Cases
        01:44
      • 8.04 Examples of Recommender System and Their Designs
        02:55
      • 8.05 Leveraging PyTorch to Build a Recommendation Engine
        02:23
      • 8.06 Collaborative Filtering and Memory Based Modeling
        06:31
      • 8.07 Item Based Collaborative Filtering
        07:02
      • 8.08 User Based Collaborative Filtering
        13:05
      • 8.09 Model Based Collaborative Filtering
        04:09
      • 8.10 Dimensionality Reduction and Matrix Factorization
        04:51
      • 8.11 Accuracy Matrices in ML
        08:06
      • 8.12 Recap
        00:52
      • 8.13 Spotlight
        03:01
  • 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
      • 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 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

      08:56
      • 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

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

Industry Project

  • Project 1

    Employee Turnover Analytics

    Create ML programs for predicting employee turnover, including data quality checks, EDA, clustering, etc. and suggesting retention strategies based on probability scores.

  • Project 2

    Segmentation of Songs

    Perform exploratory data analysis and perform cluster analysis to create cohorts of songs.

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Machine Learning Training Exam & Certification

  • 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

    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.

  • 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

    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.

  • Kirandeep Kaur

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

  • 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

    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.

  • 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

    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!

  • 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

    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.

  • 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

    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!

  • 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

    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!

  • 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.
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