Data Science and Machine Learning with Python

Experience Level : 0-10 years
  Learn at your own pace
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.
Course skills : Machine Learning | Python | Deep Learning
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 12 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 Data Science-Machine Learning.
College graduates 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 (₹3,000) : 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 : 12 Months Months that gives you flexibility to learn at your own pace.

Course Content

Statistics for Data Science
  • 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
  • Introduction to Deep Learning
  • Machine Learning VS Deep Learning
  • Introduction to Neural Networks
  • TensorFlow/Theano/Keras
  • Deep Neural networks
  • Forward propagation
  • Back Propagation Learning
  • Training & evaluation with the built-in methods
  • Activation Functions
  • Loss Functions
  • Hyperparameter Tuning
  • DropOut Regularization
  • Batch Normalization
  • Early Stopping
  • Vanishing & Exploding Gradient Descent
  • Optimization Algorithms
    • Gradient descent with momentum
    • RMS Prop
    • Adam
  • Sequential/Feed Forward Models
  • Funtional API
  • Model Saving and Reloading
  • Kaggle Projects on Deep Learning
    • Classification Project on Kaggle
    • Regression Project on Kaggle
    • Image Classification Project on Kaggle
    • Digit Classification Project on Kaggle
  • Introduction to Computer Vision
  • Convolutional Neural Networks CNN
    • Archtiecture CNN
    • Concept of Padding
    • Concept of Stride
    • CNN Layer
    • Pooling Layer
    • FC Layer
  • Residual Netoworks- ResNets
  • Case Studies
    • Le-Net5
    • AlexNet
    • VGG16
    • Google Net( Inception Networks)
  • Transfer learning
  • Image Preprocessing
  • Image Augmentation - Keras Generators
  • Callbacks API
  • Optimizers (Adam,SGD,RAMSProp..)
  • Metrics and Losses
  • Kaggle Project on Computer Vision
    • Project-1:Image Classification Project on Kaggle
    • Project-2:Digit Classification Project on Kaggle
    • Project-3:Glucoma detect blindness Project on Kaggle
  • Introduction to Natural language Processing
  • Application of NLP
  • NLTK
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop Words
  • Similarity Functions - Begging, Pasting, TFIDF
  • Word Representations
    • One Hot Encoding
    • Word Embedding
  • Learning word Embedding
    • Word2Vec
    • SkipGram , N-Gram
    • Negtaive Sampling
    • GloVe
  • Recurrent Neural Netoworks- RNN
    • BiDirectional RNN
    • Gated Recurrent Unit GRU
    • Long Short Term Memory LSTM
  • 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
  • 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 96 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

BUY COURSE

96+ Hours

Duration

50%

Discount

12 Months

Videos Access

20+

Projects

₹ 6,000 ₹3,000/-

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 become a Data Scientist?

    Data Science is all about passion and patience and It normally takes 6-8 months (daily 1-2 hours efforts minimum ) to learn all the data science concepts, algorithms and their applications. But at the same time you need to put regular efforts and complete all the projects and assignments on time to expedite the learning process. After completing this course, it is advised to take 1-2 months for self-learning and interviews preparation. Afterwards you are ready to apply Data Science Profile jobs as per your expereince level.

  • Would this program help if I have 10+ years of experience?

    Yes definitely. This is the right program for you to switch to Data Science/ML domain. Data science/AI solutions are not specific to any domain Having 10+ experience in any domain makes you the right person to engage with client, understand customer problem/business data and applying all your domain knowledge to execute and implement data collection, data preprocessing and end-end solution in a rapid manner.

  • How to align current experience with Data Science?

    Competing data science certification program at IT Bodhi is very much sufficient for entry level professionals 0-4 years to get very good job offer as Data Scientist. But when you have experience 4+ then you have to learn applied knowledge of aplication, implementation and optimization to justify your candidature for Data Science profile. This program will guide you to implement end-to-end domain specific Industry project with practical use cases and design solutions.

  • How to crack Data Science job interviews?

    Strong data science fundamentals , deep understanding of algorithms and practical real life applications. E2E Industry project completion is necessary to make sure you crack job interviews becuase this project will help you to show case your learning and its business application in real time. Also Resume Preparation, Interview Strategy and Interviews FAQs will be shared and discussed in the program.

  • Non-IT background, Am I eligible for the course?

    Yes, definitely. After doing our courses, you will have sufficient theoretical knowledge to take data science 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 proficient in python as course progress. Python is very user friendly language and can be easily learned.

  • Programming language experience required?

    The Program will use Python libraries. The necessary python skills can be easily picked up by anyone with programming experience in any language. Tutorial introduction to the Python language will be organized by Mentor and that is part of the program. Learning material on Python for self-study will also be made available.