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IIHT’s Machine Learning course is designed to help learners have a deep understanding of machine learning theory and understand how predictive models are run by systems to forecast behavior and trends using existing data. Learners will be able to evaluate machine learning algorithms and build various machine learning models and implement various regression techniques. Upon completing the course learners will gain a thorough understanding of the concepts, enabling them to analyse data to predict outcomes.


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  • Expert in building Machine Learning Models
  • Implementing Linear and Logistic Regression to predict outcomes
  • Evaluate Machine Learning Algorithms
  • Expert knowledge in going from observations to concluding about items target value
  • Forecast behavior and trends using existing data
  • Evaluate machine learning algorithms
  • Build and implement various regression techniques


Machine Learning is piercing into almost every aspect of life and work – right from spam filters to algorithmic trading, playing a crucial role in today’s IT environment. Machine learning is now becoming a mainstay in almost all forms of technology, with over 50% of firms that have implementing it in their products. Over the last few years, innovations in machine learning technologies and algorithms have resulted in fraud detection, speech recognition, self-driven cars, accurate web searches and a constantly increasing understanding of the human genome. With the rapid pace of developments in Artificial Intelligence disrupting the industry, organizations have been seeking employees that are armed with hands-on experience with Machine Learning.



  • What is Machine Learning?
  • AI v/s ML v/s DL v/s DS
  • Applications of Machine Learning
  • Types of Problems and Tasks
  • Features, Models and Design of ML Study
  • AI and ML for Banking, Finance, Healthcare and Manufacturing
  • Machine Learning with IoT and Image Processing
  • ML Services by Microsoft, AWS, IBM and Google
  • Case Studies
  • Supervised, Unsupervised and Reinforcement Learning
  • Classification and Regression
  • Clustering and Anomaly Detection
  • Recommendation Systems
  • Various Techniques and Tools
  • System Requirements
  • Revisiting Programming Language and essentials

  • Installing Tensorflow with R/Python
  • Configuring with/without GPU
  • The Programming Model of Tensorflow
  • Representing tensors
  • Creating operators
  • Executing operators with sessions
    Writing code in Jupyter
  • Using variables & Placeholders
  • Saving and loading variables


  • Visualizing data using TensorBoard
  • Understanding code as a graph
  • Implementing a moving average
  • Visualizing a moving average

  • Regression Problem Analysis
  • Mathematical modelling of Regression Model
  • Gradient Descent Algorithm
  • Learning Rate
  • Programming Process Flow
  • Use cases
  • Building simple Uni-variate Linear Regression Model
  • Multivariate Regression Model
  • Normal Equation Noninvertibility
  • Model Specification
  • Apply Data Transformations
  • Programming Using python/R

  • Over fitting and Regularization
  • L1 & L2 Regularization
  • Identify Multicollinearity in Data Treatment on Data
  • Identify Heteroscedasticity
  • Linear Regression with Tensorlfow
  • Linear Regression Implementation with Python & R
  • Predicting the Serverdowntime duration in minutes usign server dataset
  • Share Market Prediction usign dataset from quandl
  • Best Fit Line and Linear Regression
  • Do’s & Don’ts

  • Assumptions
  • Reason for the Logit Transform
  • Logit Transformation
  • Hypothesis
  • Variable and Model Significance
  • Maximum Likelihood Concept
  • Log Odds and Interpretation
  • Null Vs Residual Deviance
  • Chi Square Test
  • ROC Curve
  • Model Specification
  • Cost Function Formation
  • Mathematical Modelling
  • Use Cases
  • Digit Recognition using Logistic Regression
  • Working with datasets using R/Python

  • Metrics for regression
  • R2 Square
  • RMSE
  • MAPE
  • Metrics for classification
  • Accuracy, Precision, Recall, F1Score, Confusion Matrix
  • Metrics for probabilistic predictions
  • ROC Curve
  • Feature importance
  • Nonparametric vs. parametric analysis
  • Asymptotic approximation property
  • Streamlining workflows with pipelines
  • Using K fold cross validation to assess model performance
  • The holdout method and K fold cross validation
  • Debugging Algorithms with learning and validation curves
  • Fine Tuning Machine Learning models via grid search

  • Forming a Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Decision Tree Evaluation
  • Practical Examples & Case Study
  • CART
  • C4.5
  • Variance Analysis
  • Chi Square based Analysis
  • CART for Regression
  • Working with real time problems
  • Ensemble Technique
  • Working of Random Forest
  • Implementing Random Forest with sklearn
  • Do’s and Don’ts with Random Forest

  • Bayesian Theorem
  • Probabilities – The Prior and Posterior Probabilities
  • Conditional and Joint Probabilities Notion
  • Traditional Approach – Extract Important Features
  • Naive Approach – Independence of Features Assumption
  • Data Processing – Discretization of Features
  • Practical Examples & Case Study

  • Neurons, ANN & Working
  • Single Layer Perceptron Model
  • Multi layer Neural Network
  • Feed Forward Neural Network
  • Cost Function Formation
  • Applying Gradient Descent Algorithm
  • Backpropagation Algorithm & Mathematical Modelling
  • Programming Flow for backpropagation algorithm
  • Use Cases of ANN
  • Programming SLNN using Python
  • Programming MLNN using Python/R
  • Digit Recognition using MLNN
  • XOR Logic using MLNN & Backpropagation
  • Diabetes Data Predictive Analysis using ANN
  • Project – Banking Problem Analysis – When the customer will leave?
  • Project – Medical Problem Analysis

What do you gain from IIHT’s Blended Learning ?

IIHT’s learning model is integrated with the latest Learning trends to ensure that the audience remains engaged and their overall learning experience is flexible, convenience and productive. What more? We provide you a unique and engaging content on a user friendly and immersive learning platform that helps you to not only attend the training sessions, but watch Learning videos, read Learning Materials, interact with fellow students, write to the faculty members, practice labs, 24×7 support from a single window that makes learning effective. The assignments and assessments designed as part of the course ensures you develop right capability to prove your worth in your existing job or with prospective employer. Our state of the art learning system helps you to connect with fellow learners who are mostly working professionals that helps you to learn through collaboration and knowledge sharing.



Course participants should have knowledge of Data Science if not successfully completed the IIHT Data Science course. A good knowledge of statistics also helps. Course participants should have knowledge of Data Science if not successfully completed the IIHT Data Science course. A good knowledge of statistics also helps.

The job roles for Machine Learning are:

  • Machine Learning Engineer
  • Data Scientist

All your classes will be recorded and made available through the learning management system. You can view these videos later at your convenience.

Yes! IIHT offers an exclusive placement portal for all learners who meet certain criteria. The requirements for availing placement assistance will be notified in advanced, giving you ample time to work towards it.

You can register for the course of your choice directly from our website or head to your closest IIHT centre. You can also speak to the learning consultants, who will guide you through the process.

You can pay online. We accept net banking, UPI and most credit and debit cards. Our payment gateway also offers an EMI option if you would like to pay in instalments.

To initiate a refund you may write to us at support@iiht.com and a representative will get in touch with you soon.

When you sign up for a course, you are eligible for a discount on your next course. The discount percentage will increase with every consecutive signup. The objective of this program is to ensure that learners have an incentive to learn more without having to worry about spending too much. And hey, it is also to show you how much we treasure your association!