Machine Learning with Python

Certificate Included

Live Industry Projects & Case Studies

Machine Learning





"AI will add 2.3 million jobs by 2022"~ Gartner

Course Overview

Technology giants are rapidly migrating to an AI-ML world. The industry is getting disrupted. It is very obvious where tomorrow’s jobs are going to be. Upgrading your expertise in AI/ML is the best way to sustain your career growth and professional stability.

    Machine Learning | Deep Learning | Neural Networks | Python | Mathematics

    LIVE PROJECTS : Hands-On Industry Projects at Kaggle.
    JOB ASSISTANCE : Apply for suitable Machine Learning and AI profiles. At IT Bodhi you will get expert mentorship which will help you prepare for best of the industry jobs.
    CRACK INTERVIEWS : Learn How to Crack Machine Learning Interviews for a new job or within the same company.
    CHANGE YOUR DOMAIN : Switch your career to Crack Machine Learning with in next 4-5 months with targetted preparation.
    NO PRIOR CODING EXPERIENCE ? Do Not worry, we have dedicated modules that will help you acquire all the required skills in a very short duration.
    Advance learning with in-depth understanding of Mathematics, Statistics & Computer Science working behind all complex algorithms and thier applications.

Course Mentors

Gaurav Goel from BITS Pilani with 14+ years of industry experience

Who should take this course?

Working professionalswho are looking to build a career in AI-Machine Learning.
College graduates (B.Tech, BE, M.Tech, MS,MCA, MBA, PGDBM, Diploma etc.) who want to learn Advance Machine Learning and start career in the most exciting & highest paid technology in the industry.

Course Detail

Daily hands-onInteractive sessions Doubt Clearing sessions
Industry use cases Weekly assignments Kaggle competitions

Course Fee and Payment Process

  • Full Payment Mode : Pay full course Fee ( ₹14,500/- ) and join the course.
  • Easy EMI Mode :
    • Pay Registration Fee : ₹1,000/- and attend 1 week classes.
    • Payment after 1 week : ₹7,000/-
    • Payment after 8 weeks : ₹7,500/-
  • No Refund Policy : Refer recorded demo class videos before registering for the course which will give you fair idea about the Mentor, kind of problems being disucssed and quality of the course. No refund of any kind after fee submission.

Course Content

Statistics for Machine Learning
  • Statistics and it's applications
  • Descriptive Statistics / Infernetial Statistics
  • Measures of Central Tendency - Mean,Median,Mode
  • Measures of Dispersion - Range,IQR,Standars Deviation,Variance,Corelation
  • Measures of Position - Percentiles,Quartiles,Z Score Deviation,Variance,Correlation,Covariance
  • Probability density function
  • Probability theory and Probability Distributions
  • Normal Distribution
  • Central limit theorem
  • Hypothesis Testing
  • Test Statistics
  • T-value
  • P-value
  • Hypothesis Test for Regression Slope
  • Mathematics for Machine learning
  • -Linear Algebra
  • -Multivariate calculus
  • -Probability theory and Probability Distributions
  • -Matrices, Eigen Vectors and their application for Data Analysis.
  • Computer Science & Algorithms: Matrix implementation
  • Functions and Graphs
  • Flow, Conditions & Loops, Variables, Operations, Funcations
  • Data structures (Array, Lists,Strings, Sets, Dictionary, Tuples, Series, Tensors)
  • Data Frame Manipulation and Data Visualization.( Metplotlib, Plotly)
  • What is Machine Learning – Examples and Applications
  • Learning Numpy, Pandas, Scikit Learn Python libraries
  • Learning Matplotlib Python Library for data visualization
  • Machine Learning Algorithms
  • Cost Function
  • Metrics for Model Evaluation and Validation
  • Training and Testing
  • Model Overfitting-Underfitting
  • Bias & Variance
  • Gradient Descent Optimization & Learning Rate
  • Bias & Variance
  • Hyperparameters Tuning & Model Optimization
  • S2 Mini-Projects to understand and implement Machine Learning Basics
  • Data Wrangling
  • Data Pre-processing
  • Feature Selection
  • Feature Transformations
  • Outlier Detection and Handling
  • Handling Missing Values
  • Introduction to Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Naïve Bayes Classifier
  • Bayesian Statistics and Inference
  • K-Nearest Neighbor
  • Support Vector Machine - SVM
  • One mini project hands-on for each algorithm
  • Introduction to Unsupervised Learning
  • K-Means Clustering
  • Agglomerative Hierarchal Clustering
  • Clustering using DBSCAN
  • Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
  • -Mall Customer Segmentation
  • Machine Learning on Google Colab
  • PCA
  • LDA
  • Kernel PCA
  • k-fold Cross Validation
  • Grid Search
  • Bagging
  • ADA boost
  • XGBoost
  • Light GBM
  • Ensembling Techniques
  • Stacking
  • All ML algorithms will be covered with hands-on mini projects.
  • Capstone Projects:-
  • -Advance House price Prediction
  • -Loan Approval Classification
  • -Breast Cancer Detection
  • -Mushroom Classification
  • -IRISH Flower Multi Classification
  • -Covid19 Global Forecasting and Analysis
  • -Wine Multi Classification
  • -Diabetes Prediction
  • -Titanic Survival Project
  • -Credit Card Fraud Detection Project
  • -Mall Customer Segmentation
  • -MNIST Digits Classification
  • -Image Classification on Flower Data set
  • -Cats and Dogs Image Classification
  • Hackathons & Competitions
  • Introduction to Kaggle Platform and other Data Science Competitions
  • Kaggle and HackerRank project competitions for PrePlacement and Job offer.
  • Acing ML Interviews
  • -Booklet of all ML interview questions
  • -Showcasing and presenting ML projects in interviews?
  • -Do's-Dont's in interviews
  • -Interviews preparation
  • Resume Preparation
  • -How to make a impressive resume?
  • -Mention right ML Projects in resume
  • -A good & a Bad resume
  • Introduction to Deep Learning
  • Machine Learning VS Deep Learning
  • Introduction to Neural Networks
  • TensorFlow/Theano/Keras
  • Deep Neural networks
  • Image Classification Project using Deep Learning
  • Boston Housing Project using Deep Learning
  • Different Approaches to Deploying Machine Learning Models in Production
  • System Architecture, Component Integration and Data Pipeline
  • Batch vs. Real-time Prediction
  • Best Practices and Industry Standards
  • Deploy Machine Learning models Using Flask Rest API on Heroku Server
    • What are APIs
    • Environment Setup & Flask Basics
    • Creating a Machine Learning Model
    • Saving the Machine Learning Model: Serialization & Deserialization
    • Creating an API using Flask
    • Test Flask App Locally
    • Deploy to Heroku
    • Test Working App
"A breakthrough in machine learning will be worth ten microsoft's"~ Bill Gates

Gaurav Goel

From Oracle

Mode of Classes: Online ( ZOOM Meeting )


  Duration 42 Hours
  Pre-requisites Learning Aptitude
  Batch size 10
  Start Date 27-Aug-22
  Schedule Weekend - 2 Classes per Week
  Mode Online Live Sessions
  Instructor Experts from Oracle/Amazon
Need More Information,Please Write to Us







End Date

10:00 AM IST


12 Weeks




2 Hours





  • Payment Options

    A/C # 916020018356139 , IFSC Code : UTIB0001082
    Bank: Axis Bank | A/C Type: Current
    Share Transaction ID with Name & Phone at

  • Fee Refund Policy

    No Refund after fee deposit. Refer Trial video class recordings before registering for the course which are availble for each and every course. Recorded videos are designed to give you fair idea about the mentor and the quality of the course. After fee submission, there will be no refund of any kind. In case of any genuine issue fee can be transferred to the next batch but valid for next 2 months only.


I did my Machine Learning Training from IT Bodhi. I can bet you that IT Bodhi is the best machine learning training institute in Delhi NCR. It is the best course for beginners as well as professionals who wants to dig deep into the algorithms and concepts of Machine Learning. Ajay Sir and Rajnish Sir are the best trainers who helped me on live projects. I am truly grateful to IT Bodhi for their guidance at every step and it is hard to find mentors like them.

Kartik Tyagi

Placed at Grofers as ML Engineer

Jaypee Institute of Information Technology

I am a Mechanical engineer and wanted to change my domain to ML/Data Science. Initailly learning 'Machine learing' and getting a job in this domian was a distant dream for me. Faculty at IT Bodhi are great mentors and in just 6 months I was able to learn Machine Learning and crack ML job interviews. Great environmnent of learning and support at IT Bodhi. Any student from non IT branch who wants to learn Machine Learning - Join IT Bodhi

Kuldeep Arya

Placed at Devnagari as ML Engineer

Amity University

It is hard to find words to express my gratitude to Ajay and Rajnish sir who made Machine Learning so easy to understand with practical implementation. Reached at expert level with right guidance and got placed as ML Engineer.Highly recommended to everyone who want to make career in Data Science and Machine Learning.

Navya Singh

Placed at TCS-Digital as ML Engineer

Jaypee Institute of Information Technology

Machine Learning was completely new for me and programming was not my cup of tea. I explored many institutes and finally chose IT Bodhi and it came out be the right decision. Now ML is my passion and I am quite a coder in Python. With lot of learning and good projects,I Landed up in Capegemini as Machine Learning engineer.

Abhishek Jain

Placed at Capgemini

ABES Engineering College, Ghaziabad

Frequently Asked Questions

  • Time to become a Machine Learning Engineer?

    If you complete this cours in Machine Learning, you will definitely be able to apply and grab entry level data scientist and Machine Learning engineer roles at any product or service level companies.

  • Is this course personalized for me?

    Yes,definitely the course is personalized to each and every individual. In our regular classes, course mentors helps in clearing all the doubts and assignments and full assistance to complete E2E Industry project.

  • Knowledge of programming language required?

    The Program will use Python libraries. The necessary python skills can be easily picked up by anyone with no or little programming experience. Basic Python classes are part of the program. Self learning material on Python for will also be made available.

  • I am from Non IT/CS background, Will I face challenge?

    No. The necessary python skills can be easily picked up by anyone with no or little programming experience. After doing our courses, you will have sufficient theoretical knowledge to take machine learning interviews with hands-on experience in E2E industry project.

  • What happens if I miss a Class?

    It is recommended not to miss any class. In case it is unavoidable,refer session recording or cover missed topics in another batch.

  • Will I get a certificate after completion of courses?

    Yes, Certificate will be given at the end of the course.