Machine learning training

Introduction to ML, AI

Why we require AI and ML?

Problem with traditional Software systems

Opportunities with AI,ML

What you need to excel - only a logical mind!!

Tools and Software to efficiently build ML models

Why R and Python(with Tensorflow) is very popular ?

Your first ML model

Understand what actually is a ML model

How to handle data

Preprocessing data

Types of ML models - Supervised and Unsupervised

A peek into Reinforcement Learning

How to break your data into Training and Test

Cross validation techniques

Linear Regression

Understand Linear Regression

Gradient Descent

Do actual hands on and understand the calculations behind Gradient Descent

Brush up on Differentiation to understand the maths behind the hands on

Code both in R and Python

Learn how to improve your model

Overfitting

Overfitting is one of the most difficult aspect to learn while building a ML model

Use the above Linear Regression model to understand Overfitting

Learn with Hands on - how to avoid Overfitting

Bias Variance Tradeoff

Regularization - Ridge, LASSO

ANOVA, F tests

Logistic Regression

Understand CLassification with Logistic Regression

Maximum Likelihood Estimation

Build an end-end model with Logistic Regression using scikit Learn

Hands on - how actually you will build a model in the Industry

How to code for Interviews, Data Science Competitions

Decision Trees

What is a Probability based model and why Decision Tree is such a model? Understand the concepts of Entropy, Gini Impurity, Information Gain Do a detailed hands on project to predict the possible Loan Defaulters for a large multi-national bank

Apply the concepts of Overfitting

How to improve the Decision Tree model without Overfitting

Bagging, Boosting

Random Forest

AdaBoost, Gradient Boost

k-NN

Understand a Distance based model with kNN

how to choose the value of k

Project work on Predicting Breast Cancer

Support Vector Machines(SVM)

The power of SVM and what it can do which other models cannot

Why SVM is so popular in the industry

Learn all about Kernel Functions

Different Kernel functions

Build an OCR(Optical Character Reader) with the help of SVM and Kernel functions

Neural Networks

Why Neural Networks can actually solve any Complex pattern?

How to build the Neural Network Architecture

How Neural Network mimics the cognitive capabilities of humans

Build your own AND,OR,NOT,XOR,XNOR Logic Gates with Neural Network

Understand Forward & Backward Propagation

Plot a Neural Network with code and map your understanding between theory and practical

Change the architecture with code and see how the Neural Network behaves

Different Activation Functions

Vanishing Gradient problem

Loss functions

Deep Neural Networks

Optimization methods

Gradient Descent with Momentum, RMSProp, ADAM

Learning Rate Decay

Xavier Initialization

Introduction to Keras and Tensorflow(TF)

Deep Learning in Keras with TensorFlow as the backend

Project Work

Unsupervised Learning

Basic concepts of Clustering

k-means Clustering

Hierarchical clustering

Build a hands on project to do Social Media analysis with Clustering

 PCA

Principal Component Analysis(PCA)

Learn the maths behind PCA

Learn how to code and plot a PCA

Recommendation Engine

Understand how Netflix or any other Tech giant uses Recommendation Engine

Content and Collaborative Filtering

Pros and Cons of different approaches of Recommendation Engine

Market Basket Analysis

Apriori Rules

Build your own Recommender System

Computer Vision

Image Detection, Image Classification, Localization

Introduction to Convolutional Neural Networks(CNN)

Build a Handwritten Digit recognizer with CNN

Strides, Padding concepts

Convolutional, Padding and Fully Connected layers

Sliding Window

Edge Detection

Advanced Computer Vision

YOLO ALgorithm - You Only Look Once

Introduction to classical networks like LeNet5

IoU

Build an Image Classifier with CNN

Data Augmentation Techniques

Natural Language Processing(NLP)

Introduction to Natural Language Processing(NLP)

Text Preprocessing

Lemmatization, Stemming

Syntactical Parsing, Entity Parsing

CTopic Modelling with Latent Dirichlet Allocation(LDA)

Collapsed Gibbs Sampling

Word Embedding with Word2Vec - CBOW and SkipGram models

Restricted Boltzman Machines

Recurrent Neural Network(RNN) and Long Short Term memory(LSTM)

Build a chatbot with the above concepts of NLP and Neural Networks

Introduction to AI

History of AI

State of the Art AI

Types of Agents

Types of Environments

Asymptotic Notations

Search

Uninformed Search-Breadth first search

Uniform Cost Search

Depth First Search

Depth-Limited Search

Iterative Deepening Depth-First Search

Bidirectional Search

Informed Search-Greedy Best-First Search

A* Search

Beyond Local Search-Hill Climbing

Simulated Annealing

Beam Search

Genetic Algorithms

Online Search

Informed Search

Adversarial Search

Min-max Tree

Alpha-Beta Pruning

Move Ordering

Stochastic games

Constraint Programming

Constraint Satisfaction Problems

Map coloring

Sudoku

Job scheduling Constraint Propagation

Backtracking

Reinforcement learning

Passive Reinforcement learning

Active Reinforcement learning

Policy Search