Full-Stack Machine Learning Program

Start your Data Science journey by learning the core data science concepts, along with applying them in exclusive real-time cases, from industry experts and experienced faculties.

Course Overview

Learn essential data science techniques using Python to analyze the data and help make your organization take important decisions. Explore popular classification, regression, clustering, etc. algorithms using many hands-on exercises. By the end of this module, you will be able to transform your theoretical knowledge into practical by looking at real-life business scenarios.

Comprehensive 40 weeks program
100% live online classes
Live hands-on practice sessions
Online support from industry experts
Real-life industry challenges
Class recordings
Learning materials

What you will learn:

Coding is for Everyone

Introduction to Data Science, ML, AI

What is Data Science, ML, AI
Why Data Science, ML, AI
Business Case Studies
How to prepare for a career switch
Freshers: How to study for interviews
Experienced: How to study for interviews

Statistics without Stress

Why do we need statistics for Data Science?
Measures of Central Tendencies
of Dispersion
Central Limit Theorem
How do I apply these concepts?
Commonly used distributions – Binomial, Poisson, Geometric and Normal
Probability Theory
Why do we need probability in Data Science?
Naive Bayesian
Hypothesis Testing
Can I apply them in real life? Yes

Statistical Modeling

Linear Regression
Understanding how Linear Regression (OLS) works
Easy-to-Understand Maths for Linear Regression
Data Encoding – Why Encoding? How to Encode? What Encoding?
Assumption of Normality
Assumption of Autocorrelation
Assumption of Heteroskedasticity
Interpreting A-Z of Linear Regression Output
Feature Engineering
Understanding Performance Metrics
Hands-on: Basic Case Study
Hands-on: Advanced Case Study demonstrating real-life challenges
Logistic Regression
Understanding how Logistic Regression (MLE) works
Easy-to-Understand Maths for Logistic Regression
Interpreting A-Z of Logistic Regression Output
Understanding Performance Metrics
Hands-on: Basic Case Study
Hands-on: Advanced Case Study demonstrating real-life challenges

Introduction to Machine Learning

Types of Learning
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
How do I know which algorithm to apply?
Understanding Bias & Variance
Understanding Underfitting & Overfitting
How do I overcome Bias & Variance in real projects?
Understanding Loss Functions for different problems
Understanding & fine-tuning Hyperparameters
Understanding Cross-Validation
Optimization Techniques
Local Optimization vs Global Optimization
Stochastic Gradient Descent
Batch & Mini-Batch Gradient Descent
Momentum, AdaGrad, RMSProp
ADAM Optimization
Hands-on: Revisiting Linear Regression with Optimization Algorithms
Hands-on: Revisiting Logistic Regression with Optimization Algorithms
Why do I need Optimization Techniques?

Predictive Modeling with ML Algorithms

Decision Tree for Supervised Learning Problems
Understanding how Decision Tree works
Easy-to-Understand Maths for Decision Tree
Understanding Gini Index and Information Entropy
Hands-on: Case Study using Decision Tree Algorithm
Hands-on: Overcoming Challenges in Decision Trees
Practical Key Takeaways
K-Nearest Neighbour
Understanding how KNN works
Understanding the distance metrics
Easy-to-Understand Maths for KNN
Determining when can we not use KNN appropriately
Support Vector Machines
Understanding how SVM works
Easy-to-Understand Maths for SVM
Understanding when & how SVM outperforms other algorithms
Understanding SVM Kernels
Hands-on: Case Study with SVM & SVM Kernels
Understanding when do you not need a kernel
Understanding which kernel to use

Unsupervised Learning

K-Means Clustering
Revisiting Distance Metrics
Understanding how K-Means Clustering works
Easy-to-Understand Maths for K-Means Clustering
Hands-on: Clustering using K-Means Algorithm
Practical Applications
Hierarchical Clustering
Understanding how Hierarchical Clustering works
Easy-to-Understand Maths for Hierarchical Clustering
Hands-on: Clustering using Hierarchical Algorithm
Practical Applications
Apriori Algorithm
Understanding how Apriori Algorithm works
Easy-to-Understand Maths for Apriori Algorithms
Understanding Metrics for Association Rules
Hands-on: Market Basket Analysis using Apriori Algorithm
Practical Applications

Ensemble Machine Learning

Random Forest
Understanding how Random Forest works
Why Random Forest?
Random Forest Hyperparameters
Fine-tuning Hyperparameters using Grid-Search
Hands-on: Case Study
Boosting Algorithms
Understanding how Boosting Algorithms works
Easy-to-Understand Maths behind Boosting Algorithms
Deep dive into Adaboost Algorithm
Deep dive in Gradient Boosting Method
Hands-on: Case Study on Adaboost & gradient Boosting Method
Heterogeneous Ensembles
Stack Generalization

Neural Networks

Deep Learning

Convolutional Neural Networks
Convolution network vs plain neural network
Convolution
Locally Connected Layer
Transitional Invariance
Convolutions (Discrete 1D)
Spatial dimension – convolutions
Convolution- backward
Recurrent Neural Networks
What are RNNs?
Forward pass in RNN
Backward pass in RNN
Vanishing and exploding gradient problem
Different types of RNN architecture
Bi-directional RNNs
Advantages and disadvantages of RNN
Application area of RNN

Entering into the world of AI

Natural Language Processing
NLP Foundation
Cleaning Text Manually
Cleaning Text using NLTK
Preparing Text with Scikit-Learn
Preparing Text with Keras
Bag of Words
Basic Neural Model for Sentiment Analysis
The n-gram CNN Model for Sentiment Analysis
Word Embeddings
Text Classification using CNN
Language Modeling
Develop a Neural Language Model for Text generation
Image Captioning using Pre-Trained Models
Computer Vision
Introduction to Computer Vision
Working with Images
Manipulating Images with PIL
Manipulating Images with Keras
Scaling Images with Keras
Image Augmentation
Manipulating & Ordering Image Channels
How Convolutional Neural works
Using Filter, Padding, Stride, Pooling
ImageNet Large Scale Visual Recognition
Milestone Model Architectures Implementations
Managing Model Complexity using 1X1 Convolutions
Using Pre-Trained Models, Transfer Learning
Image Classification
Object Detection
Face Recognition

Model Deployments

Understanding Git
ML Model Persistence
ML Model Deployments using Python frameworks
ML Model Deployments using Cloud Platform

Building Models in distributed computing

Understanding Big Data Ecosystem
Learning PySpark
Building ML Models using PySpark

Schedule

Weekdays (Mon – Fri)
Mar 21 – Dec 21, 2022

Training Timing:
07:00 AM – 09:00 AM (IST)

Month 1 (Mar 21 - Mar 31)
Month 2 (Apr 1 - Apr 29)
Month 3 (May 2 - May 31)
Month 4 (June 1 - June 30)
Month 5 (July 1 - July 29)
Month 6 (Aug 1 - July 31)
Month 7 (Sept 1 - July 30)
Month 8 (Oct 3 - Oct 31)
Month 9 (Nov 1 - Nov 30)
Month 10 (Dec 1 - Dec 21)

Faculty Member

<img width="150" height="150" src="https://algoritmolab.com/wp-content/uploads/2020/06/AI_faculty_Dipayan_Sarkar-150x150.jpg" class="attachment-thumbnail size-thumbnail" alt="Dipayan Sarkar" srcset=" https://algoritmolab.com/wp-content/uploads/2020/06/AI_faculty_Dipayan_Sarkar-150x150.jpg 150w, https://algoritmolab.com/wp-content/uploads/2020/06/AI_faculty_Dipayan_Sarkar-300x300.jpg 300w, https://algoritmolab.com/wp-content/uploads/2020/06/AI_faculty_Dipayan_Sarkar.jpg 368w " sizes="(max-width: 150px) 100vw, 150px" />

Senior Data Scientist, Ex-Maersk

Adjunct Faculty, Great Learning, Chennai and BML Munjal University, Gurgaon

Author, Ensemble Machine Learning (PACKT Publishing)

Author, Deep Learning with R (PACKT Publishing)

With over 20 years of experience, Dipayan has a wealth of knowledge under his belt when it comes to analytics in real-world situations. Prior to being an independent consultant and a mentor in the Data Science, Machine Learning (ML), and AI space with various organizations, universities, and educational institutions, he served in the capacity of a senior data scientist with various Fortune 500 companies. He also holds a Masters in Economics and is currently pursuing doctoral program in artificial intelligence from Rennes School of Business, France

Over the last decade, he has focused his analytics expertise in the fields of Data Science, ML, and AI as he has found how passion, technology, and analytics can be combined to create products that are essential in today’s business environment. Dipayan takes a keen interest in the mathematics behind the ML techniques.

While consulting for corporations on their Data Science and Analytics requirements, he is also a current content developer, mentor, and trainer in the Data Science, ML, and AI space with premier institutions in India & Singapore.

Course Certificate

Start your journey today!

₹ 206500