Build Machine Learning Expertise
with the Leader in AI and Robotics Research

• Numpy
• Scipy
• Pandas
• Matplotlib

• Demystifying ML
• Modern AI

• Supervised vs Unsupervised Learning
• Classification vs Regression
• Training and Testing Methods
• ML Pipeline

• Data Munging
• Outlier Detection
• Hands-on Data Visualization

• Feature Selection Methods
• Feature Extraction Methods

• k-NN Classification
• Decision Tree
• Linear Classifier

• Linear Regression
• Polynomial Regression
• Logistic Regression

• Basic Mathematics and Hands-on Analysis

• Understanding standard NLP Libraries
• Exploring Different Representations Methods

• Bootstrapping
• Bagging

• Literacy Rate Prediction
• Aptitude Questions Classification

• NLP Problem Formulation and Fundamental Solutions

• Exploring Word Embeddings

• Methods to Measure the Efficacy of the ML models

• Single Layer Perceptron
• Basic Mathematics
• Practical Tricks and Tips

• Activation Functions
• MLP Architecture

• PCA Algorithm
• Underlying Mathematics
• Applications of PCA

• SVM Formulation
• Non-linear Mapping
• Popular Kernels

• Non-Linear Dimensionality Reduction for visualization
• Hierarchical Agglomerative Clustering
• K-means clustering

• Classical and Modern Feature Representation for Visual Data
• Classical and Modern Feature Representation for Audio/Speech

• Author Identification
• Customer Segmentation

• Deep Learning Framework
• Neural Network Module
• Training a Neural Network

• Basics of Pytorch
• Pytorch Packages
• Computation on GPU
• Model Building
• Model Training and Evaluation

• Convolutional Layers and Backpropagation
• Building Blocks for Convolutional Networks
• Applications of CNN - Audio, Text and Image

• Intro to Autoencoders
• Deep Autoencoder
• De-noising Autoencoder

• Recurrent Neural Network Architecture
• Machine Learning with Sequential Data
• Neural Network Language Models

• Intro to Keras and Keras Basics
• Keras Models
  - Sequential Model
  - The Functional API
  - Model Subclassing
• Sequential ,odel Steps and it's Parameters and Adding Layers
• Building a Network in Keras
• Loading and Save the Models with Example and Measuring the Performance

• ETL
• Dimensional Data Modelling
• Big Data Query

• Memorization and Generalization
• Occam’s Razor
• Ways to Prevent/Reduce Overfitting

• Classical Models (AR, MA, ARMA & ARIMA)
• Neural Networks for Time Series Forecasting

• Random Forest
• Boosting and Adaboost

• Different Roles for Human in the Loop
• Users in the ML Systems
• Relevance Feedback
• Active Learning

• Image Classification
• Identification of plant disease from leaf images

• Visualizing CNNs
• Improving CNNs
• Popular CNN Architectures

• Vanishing Gradients
• Understanding Recurrent Architectures
• RNN, LSTM, GRU

• Introduction to Recommendation Systems
• Social Recommender Systems
• Collaborative Filtering
• Hybrid Recommendation Models

• Adapting Pre-trained Models for Desired Applications

Machine Learning Deployment using

• Docker
• HTML
• Java Script
• Joblib and Pickle

• Technical and Non-technical Pratical Issues in ML

• Model Compression
• Pruning
• Quantization
• Student-teacher Network

• Reinforcement Learning Framework
• Single and Multi agent RL
• Q-Learning
• Deep RL
• RL Applications

• Siamese Architecture
• Generative Adversarial Networks

• Introduction to Computer Vision
• Camera Model and Geometry
• Problems in Computer Vision
• Applications and Use Cases

ML in NLP

• Sequence Generation and Information Extraction
• Latest Text Embedding Models
• Neural Machine Translation
• Attention Mechanism for DL

ML in Speech

• DNN based Speech Applications
• DNN with Attention
• Sequential Network
• Encoder Decoder Models for Speech Recognition
• Automatic Speech Recognition

• Talks from different domain experienced people

• Retail sales forecasting
• Image processing and Transfer Learning