Machine Learning-AI with Python

Experience Level : 0-10 years
  Learn at your own pace
  Weekly live doubt session with mentor
Machine Learning

LINEAR REGRESSION EXPLAINED

MACHINE LEARNING ALGORITHMS

NEURAL NETWORKS EXPLAINED

DEEP LEARNING ALGORITHMS

"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.
Unique Hybrid Course 12 Hours Instructor led Live Online Sessions + 65 Hours Recorded Videos + 10 Projecrs.
Course skills : Machine Learning | Python | Deep Learning
Advance Certification Program.
Course starts from scratch and takes you to expert level : Start from foundation classes in mathematics and Python and reach at advanced machine learning and AI concepts such as Begging & Boosting,XGBoost,Ensebmling and PCA/LDA.
Start Competing at Kaggle : From very first algorithms start working on Live Kaggle projects and build rich online Github/Kaggle profile.
Blended Learning Pedagogy : with Minimal Disruption To Work Schedule.
50% Theory : Making strong foundation and clearing all advanced concepts in Machine Learning Application.
50% Projects Hands-On : Pratical learning with hands-On Industry Projects & Live Competitions at Kaggle.
Learn cutting-edge applications of learned concepts through industry projects created under guidance of industry experts.
Apply for suitable Machine Learning and AI profiles and you will get expert mentorship which will help you prepare for best of the industry jobs.
Language of Communication : ENGLISH + HINDI
LEARN AT YOUR OWN PACE : You have access to course videos for 6 Months + 1 Months that gives you flexibility to learn at your own pace and do a lot of practice to become master in application.
In-depth thorough understanding of Mathematics, Statistics and Computer Science concepts working behind complex algorithms and their 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

Course Fee and Payment Process

  • Go through Demo Class videos : Like the videos and want to join the course !
  • Pay Full Fee (₹1,500) : Get instant access to full course videos.
  • First go through demo videos and if you like the videos, make the payment for full access. After payment No Refund of any kind.
Duration of Video Access : 6 Months Months that gives you flexibility to learn at your own pace.

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
  • Numpy and Pandas Tutorial
  • Scikit Learn Tutorial
  • 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
  • Boston Housng Price Prediction Project to understand basic concepts
  • 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
  • Live project hands-on on Kaggle Data Sets
  • -Advance House price Prediction
  • -Loan Approval Classification
  • -Breast Cancer Detection
  • -Mushroom Classification
  • -IRISH Flower Multi Classification
  • -Wine Multi Classification
  • -Diabetes Prediction
  • -Titanic Survival Project
  • -Credit Card Fraud Detection Project
  • Introduction to Unsupervised Learning
  • K-Means Clustering
  • Agglomerative Hierarchal Clustering
  • Clustering using DBSCAN
  • Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
  • Customer Segmentation Project
  • Feature Selection VS Feature Extraction Techniques
  • PCA
  • Kernel PCA
  • LDA
  • t-SNE
  • MNIST Digit Classification Project
  • k-fold Cross Validation
  • Grid Search
  • Bagging
  • ADA boost
  • XGBoost
  • Light GBM
  • Ensembling Techniques
  • Stacking
  • Oversampling
    • - SMOTE (Synthetic Minority Oversampling Technique)
    • - Borderline SMOTE
    • - ADASYN (Adaptive Synthetic Over Sampling)
  • Undersampling
    • - Tomek links
    • - Cluster Centroids
    • Cost-sensitive classifiers
    • class-specific weights
      • Loan Approval Project
  • All ML algorithms will be covered with hands-on mini projects
  • 15-20 small projects
  • Major Projects:-
  • -Advance House price Prediction
  • -Loan Approval Classification
  • -Breast Cancer Detection
  • -Mushroom Classification
  • -IRISH Flower Multi Classification
  • -Wine Multi Classification
  • -Diabetes Prediction
  • -Titanic Survival Project
  • -Credit Card Fraud Detection Project
  • -MNIST Digits Classification
  • -Customer Segmentation Project
  • Hackathons & Competitions
  • Introduction to Kaggle Platform and other Data Science Competitions
  • Kaggle and HackerRank competitions to grab PrePlacement and Job offer.

Learn from the experts

Mentor

Gaurav Goel


From BITS Pilani

Summary

  Duration 65 Hours
  Pre-requisites Class 10 Mathematics + Basic understanding of any Programming Language
  Learning Schedule Learn at Your Pace
  Mode Recorded Videos
  Instructor Experts from Oracle/Amazon
Need More Information,Please Write to Us

UPCOMING BATCHES

65 Hours

Duration

85%

Discount

6 Months

Videos Access

10+

Projects

₹ 10,000 ₹1,500/-

Fee

MY SUCCESS STORY

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


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


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


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. Learning with live projects helped me a lot in getting into Capegemini in campus placement.

Abhishek Jain

Placed at Capgemini


Frequently Asked Questions

  • How much time will it take to be a Machine Learning Engineer?

    Approx. 3-4 Months. If you complete this course in Machine Learning, you will definitely be able to apply and grab Machine Learning engineer roles at any product or service level companies.

  • Is this course personalized for me?

    Yes,definitely the course is personalized for each and every individual. Course is designed by experts and lectures are very interactive to make learning interesting and engaging.

  • Is any specific programming language experience 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 Branch, will this program help me to get job in Machine Learning?

    Yes, definitely. After doing our courses, you will have sufficient theoretical knowledge to take machine learning interviews with hands-on experience in E2E industry project. Python is part of the program and Mentor will help you in learning programming basics and gradually you will be profecient in python as course progress. Python is very user friendly language and can be easily learned.