Module 1 – Introduction to Machine Learning

• Basic Concept
• Train, Test & Validation Distribution
• ML Strategy
• Computation Graph
• Evaluation Metric
• Human Level Performance

Module 2- Machine Learning

Supervised

• Linear Regression
• Logistic Regression
• Gradient Descent
• Decision Tree
• Random Forest
• Bagging & Boosting
• KNN

Unsupervised

• K-Means
• Hierarchal Clustering

Module 3 – Python Programming

Python

• Basic Programming
• NLP Libraries
• Open CV

Module 4 – Mathematics Foundation

Basic Statistics

• Sampling & Sampling Statistics
• Hypothesis Testing

Calculus

• Derivatives
• Optimization

Linear Algebra

• Function
• Scalar-Vector-Matrix
• Vector Operation

Probability

• Space
• Probability
• Distribution

Module 5 – Intro to Neural Network & Deep Learning

Introduction

• Intro
• Deep Learning Importance [Strength & Limitation]
• SP | MLP

Feed Forward & Backward Propagation

• Neural Network Overview
• Neural Network Representation
• Activation Function
• Loss Function
• Importance of Non-linear Activation Function
• Gradient Descent for Neural Network

Module 6 – Parameter & Hyperparameter

Practical Aspect

• Train, Test & Validation Set
• Vanishing & Exploding Gradient
• Dropout
• Regularization

Optimization

• Bias Correction
• RMS Prop
• Adam, Ada, AdaBoost
• Learning Rate
• Tuning
• Softmax

Module 7 – Data Processing

Environment

• Scikit Learn
• NLTK
• Spacy & Gensim
• OpenCV
• Tensorflow
• Keras

Text Processing

• Representation
• Data Cleaning
• Data Preprocessing
• Similarity

Image Processing

• Image
• Image Transformation
• Filters
• Noise Removal
• Correlation & Convolution
• Edge Detection
• Non Maximum Suppression & Hysterisis
• Fourier Domain
• Video Processing

Speech Data Analytics

Feature Extraction

• Image Feature
• Descriptors

Object Detection

• Detection & Classification

Module – 8 CNN      

CNN

• Computer Vision
• Padding
• Convolution
• Pooling
• Why Convolution

Deep Convolution Model

• Case Studies
• Classic Networks
• Inception
• Open Source Implementation
• Transfer Learning

Detection Algorithm

• Object Localization
• Landmark Detection
• Object Detection
• Bounding Box Prediction
• Yolo

Face Recognition

• What is Face Recognition
• One Shot Learning
• Siamese Network
• Triplet Loss
• Face Verification
• Neural Style Transfer
• Deep Conv Net Learning

Module 9 – RNN

• Why Sequence Model
• RNN Model
• Backpropogation through time
• Different Type of RNNs
• GRU
• LSTM
• Biderectional LSTM
• Deep RNN
• Word Embedding
• Debiasing
• Negative Sampling
• Elmo & Bert
• Beam Search
• Attention Model

Module 10 – Generative Adversial Network

• Autoencoders & Decoders
• Adversial Network
• Active Learning

Module 11 – Reinforcement Learning

• Q Learning
• Exploration & Exploitation

Introduction to Machine Learning

• Business Case evaluation
• Data requirements and collection
• Evaluation metrics

Machine Learning

• Profit of 50_startups data prediction
• Extra marital affair prediction
• Fraud data analytics
• Fabric sales analysis
• Classification of animals data
• Crime data analysis using clustering method and airlines data to obtain optimum number of clusters.

Python Programming

• Resource Information Analysis
• Text Cleaning of Customer reviews using NLP
• Image Manipulation (Loading, Rotation etc.)

Mathematics Foundation

• Sampling & Sampling Statistics
• Hypothesis Testing
• Calculus Problems
• Linear Algebra Problems
• Probability Problems

Intro to Neural Network & Deep Learning

Parameter & Hyperparameter

• Risk Evaluation
• Prediction of claim amount
• Emotor temp prediction
• User Behavioural Pattern

(2 ANN assignments+ 2 Parameter and hyperparameters)

Data Processing

• User review data load and familiarity with data and environment
• E commerce Product Similarity
• Sentiment classification of movie reviews
• Emotion Mining of user reviews”
• Vehicle edge detection
• Cleaning of hand-written digits data
• Image data Augmentation
• Facial feature detection
• Image data wrangling for classification
• Video Analysis of a short film
• Speech data Analysis w.r.t emotion

CNN

• Ecommerce product image classification
• Disease prediction based on images

(2 CNN algorithms)

• Vehicle identification(Object Detection)
• Animal Classification(Object Classification)
• Spatial Image classification (Image segmentation)
• Face detection
• Face recognition (Attendance using facial recognition)

RNN

• Next word prediction (Vanilla RNN)
• Twitter data analysis using Named Entity Recognition(NER)
• Retail data – Word2vec
• NER and Forecasting of Oil price prediction
• Auto text composer (NER language model)
• Auto text composer (NER language model)
• Q and A Chatbot
• Real life voice Recognition

Generative

• Machine Translation
• New Image generation based on existing images

Reinforcement Learning

• Game Intelligence

Module 13- Projects

1.Chatbot project

• Build end to end chatbot right from data storage schema to final output for a domain

2.Emotion Analytics

• Identifying and analyzing the full spectrum of human emotions including mood, attitude and emotional personality.

3.Object Detection

• Detection of objects in images

4.Face detection from CC camera feed

• Analysis of video feed from CC cameras