Price 20,000.00 GST

Course Features
Language: English
40 hours
Study Level: Intermediate
Certificate of Completion

Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. If launching a Machine career in Machine Learning sounds right up your alley, Simplilearn’s online Machine Learning Certification Training will definitely Learning help you comprehend and master concepts like regression, clustering, classification, and prediction.

Mike Tamir

No. 1 AI & Machine Learning Influencer, Head of Data Science – Uber ATG

Named by Onalytica as the No.1 influencer in AI & Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

Who can enroll for the program

Machine Learning career opportunities are exploding worldwide. Many organizations are investing heavily in Machine Learning capabilities to maintain a cutting edge in the market. The Machine Learning training course will be of benefit to the following professional roles:

Prerequisite knowledge for this course are fundamentals of Python programming, high school mathematics and basics of statistics.

Software Developers

Data Scientists

Engineering Graduates

Analytics Information Managers Architects Business Analysts Technical Project Managers

Machine Why should you Learning enroll for this program


  • Classify the types of learning including supervised and unsupervised
  • Identify the various applications of machine learning algorithms
  • Perform supervised learning techniques: linear and logistic regression
  • Understand classification data and models
  • Create robust Machine Learning models
  • Implement different Regression models
  • Choose the best algorithms among many for any given Machine

Learning problem

  • Make accurate predictions and powerful analysis
  • Use unsupervised learning algorithms including deep learning,

clustering, and recommendation systems

Program features 32+ hours of trainer led virtual classrooms 

  • Master concepts of Supervised & Unsupervised Learning
  • Practical application of algorithms in Machine Learning
  • Have a great intuition of many Machine Learning models

Chapter level details:

Lesson 1: Introduction to Artificial Intelligence and Machine Learning

Learning objectives: In this lesson, you will recognize the importance of data economy and understand the emergence and applications of Artificial Intelligence and Machine Learning.


❏ Artificial Intelligence

❏ Machine Learning

❏ Machine Learning algorithms

❏ Applications of Machine Learning

Lesson 2: Techniques of Machine Learning

Learning objectives: In this lesson, you will take a step further, to understand the techniques of Machine Learning such as supervised, unsupervised, semi-supervised and reinforcement learning.


❏ Supervised learning

❏ Unsupervised learning

❏ Semi-supervised and Reinforcement learning

❏ Bias and variance trade-off

❏ Representation learning

Lesson 3: Data Preprocessing

Learning objectives: In this lesson, you will learn how to prepare the data for machine learning algorithms with feature engineering, feature scaling, data sets and dimensionality reduction.


❏ Data preparation

❏ Feature engineering

❏ Feature scaling

❏ Datasets

❏ Dimensionality reduction

Lesson 4: Math Refresher

Learning objectives: This lesson will take you into your past and help you brush up on those math and statistics concepts highly necessary to understand the Machine Learning algorithms.


❏ Concepts of linear algebra

❏ Eigenvalues, eigenvectors, and eigendecomposition

❏ Introduction to Calculus

❏ Probability and statistics

Lesson 5: Regression

Learning objectives: In this lesson, you will unleash the real power of Machine Learning with polynomial regression, linear regression, random forest, decision tree regression, gradient descent, and regularization.


❏ Regression and its types

❏ Linear regression: Equations and algorithms

Lesson 6: Classification

Learning objectives: In this lesson, you will learn about classification, logistic regression, K-nearest neighbors, support vector machines, and Naive Bayes.


❏ Meaning and types of classification

❏ Logistic regression

❏ K-nearest neighbors

❏ Support vector machines

❏ Kernel support vector machines

❏ Naive Bayes

❏ Decision tree classifier

❏ Random forest classifier

Founded in 2009, Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies. Based in San Francisco, California and Bangalore, India, Simplilearn has helped more than 500,000 students, professionals and companies across 200 countries get trained, upskilled, and acquire certifications.

Lesson 7: Unsupervised learning: Clustering

Learning objectives: In this lesson, you will learn and implement a few more algorithms within the unsupervised learning category.


❏ Clustering algorithms

❏ K-means clustering

Lesson 8: Introduction to Deep Learning

Learning objectives: This last lesson of the course, gives you a peek into the world of deep learning and how it is related to machine learning.


❏ Meaning and importance of Deep Learning

❏ Artificial Neural Networks

❏ TensorFlow


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