AI & ML Training

The primary and overall objective of this course is to give a hands-on experience of AI ML Programming & Algorithm.

• Module I: Introduction to Python
   Topics:
  • Overview of Python
  • The Companies using Python
  • Other applications in which Python is used
  • Discuss Python Scripts on UNIX/Windows
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the screen

  Module 2: Sequences and File Operations

  Topics:

  • Python files I/O Functions
  • Lists and related operations
  • Tuples and related operations
  • Strings and related operations
  • Sets and related operations
  • Dictionaries and related operations

  Module 3: Deep Dive – Functions and OOPs

  Topics:

  • Functions
  • Function Parameters
  • Global variables
  • Variable scope and Returning Values
  • Lambda Functions
  • Object Oriented Concepts

  Module 4: Working with Modules and Handling Exceptions

  Topics:

  • Standard Libraries
  • Modules Used in Python (OS, Sys, Date and Time etc.)
  • The Import statement
  • Module search path
  • Package installation ways
  • Errors and Exception Handling
  • Handling multiple exceptions

  Module 5: Introduction to NumPy& Pandas

  Topics:

  • NumPy – arrays
  • Operations on arrays
  • Indexing slicing and iterating
  • Reading and writing arrays on files
  • Pandas – data structures & index operations
  • Reading and Writing data from Excel/CSV formats into Pandas

  Module 6: Data visualization

  Topics:

  • matplotlib library
  • Grids, axes, plots
  • Markers, colours, fonts and styling
  • Types of plots – bar graphs, pie charts, histograms
  • Contour plots

  Module 7: Data Manipulation

  Topics:

  • Basic Functionalities of a data object
  • Merging of Data objects
  • Concatenation of data objects
  • Types of Joins on data objects
  • Exploring a Dataset
  • Analyzing a dataset

  Module 8: GUI Programming

  Topics:

  • Ipywidgets package
  • Numeric Widgets
  • Boolean Widgets
  • Selection Widgets
  • String Widgets
  • Date Picker
  • Color Picker
  • Container Widgets
  • Creating a GUI Application

  Self-paced Module: Network Programming and Multithreading

  Topics:

  • MySQL DB access
  • Network programming
  • Multithreading Hands-on/Demo
  • Database Creation
  • CRUD Operations
  • Network Creation
  • Multithreading

  Module 9: Developing Web Maps and Representing information using Plots

  Topics:

  • Use of Folium Library
  • Use of Pandas Library
  • Flow chart of Web Map application
  • Developing Web Map using Folium and Pandas
  • Reading information from Dataset and represent it using Plots

  Module 10: Computer vision using OpenCV and Visualisation using Bokeh

  Topics:

  • Beautiful Soup Library
  • Requests Library
  • Scrap all hyperlinks from a webpage, using Beautiful Soup & Requests
  • Plotting charts using Bokeh
  • Plotting scatterplots using Bokeh
  • Image Editing using OpenCV
  • Face detection using OpenCV
  • Motion Detection and Capturing Video

   

  Machine Learning with Python

  Module 1: Introduction to Data Science

  Topics:

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Python

  Module 2: Data Extraction, Wrangling, Visualization

  Topics:

  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data

  Module 3: Introduction to Machine Learning with Python

  Topics:

  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent

  Module 4: Supervised Learning – I

  Topics:

  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?

  Module 5: Dimensionality Reduction

  Topics:

  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA

   

  Module 6: Supervised Learning – II

  Topics:

  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification

  Module 7: Unsupervised Learning

  Topics:

  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How K-means algorithm works?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?

  Module 8: Association Rules Mining and Recommendation Systems

  Topics:

  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How Recommendation Engines work?
  • Collaborative Filtering
  • Content Based Filtering

  Module 9: Reinforcement Learning

  Topics:

  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • 𝛼 values

  Module 10: Time Series Analysis

  Topics:

  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF

  Module 11: Model Selection and Boosting

  Topics:

  • What is Model Selection?
  • Need of Model Selection
  • Cross – Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

   

  Graphical Models

  Module 1: Introduction to Graphical Model

  Topics:

  • Add examples where Graphical Models are used (Netflix or Amazon or Facebook)
  • Why do we need Graphical Models?
  • Introduction to Graphical Model
  • theory
  • How does Graphical Model help you deal with uncertainty and complexity?
  • Types of Graphical Models
  • graph
  • Graphical Modes
  • Networks
  • Components of Graphical Model
  • Qualitative specification
  • Quantitative specification
  • Representation of Graphical Models
  • Inference in Graphical Models
  • Learning Graphical Models
  • Decision theory
  • Applications

  Module 2: Bayesian Network

  Topics:

  • What is Bayesian Network?
  • Advantages of Bayesian Network for data analysis
  • Bayesian Network in Python Examples
  • Independencies in Bayesian Networks
  • Criteria for Model Selection
  • Relative Posterior Probability
  • Local Criteria
  • Building a Bayesian Network

  Module 3: Markov’s Networks

  Topics:

  • Example of a Markov Network or Undirected Graphical Model
  • Markov Model o Markov Chain
  • Continuous-time Markov Chain
  • Reversible Markov Chain
  • Markov Property
  • Markov and Hidden Markov Models
  • The Factor Graph
  • Independencies in Markov Networks
  • Markov Decision Process
  • Decision Making under Uncertainty
  • Decision Making Scenarios

  Module 4: Inference

  Topics:

  • Inference
  • Marginal Inference
  • Posterior Inference
  • MAP Inference
  • Complexity in Inference
  • Exact Inference
  • Approximate Inference
  • Monte Carlo Algorithm
  • Gibb’s Sampling
  • Inference in Bayesian Networks
  • Inference in Bayesian Networks

  Module 5: Model learning

  Topics:

  • General Ideas in Learning
  • Goals of Learning
  • Density Estimation
  • Predicting the Specific Probability Values
  • Knowledge Discovery
  • Parameter Learning
  • Maximum Likelihood Estimation
  • Maximum Likelihood Principle
  • The Maximum Likelihood Estimate for Bayesian Networks
  • Learning with Approximate Inference
  • Structure learning
  • Constraint-based Structure Learning
  • Score-based Structure Learning
  • The likelihood Score o Bayesian Score
  • Model Learning: Parameter Estimation in Bayesian Networks
  • Model Learning: Parameter Estimation in Markov Networks

   

  Reinforcement Learning

  Module 1: Introduction to Reinforcement Learning

  Topics

  • Branches of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • What is Reinforcement Learning?
  • Reinforcement Learning – How does it differ from other machine learning paradigms
  • Comparing RL with other ML techniques
  • Elements of Reinforcement Learning
  • The Reinforcement Learning Process
  • Rewards
  • The central idea of the Reward Hypothesis
  • Reward Examples
  • Agent and Environment
  • Fully Observable Environments
  • Partially Observable Environments
  • RL Agent Components (Value-based, Policy-based, Model-based)
  • RL Agent Taxonomy
  • Types of Tasks (Episodic and Continuous Tasks)
  • Ways of Learning (Monte Carlo Approach and Temporal Difference Learning)
  • Exploration and Exploitation Trade off
  • Approaches to Decision Making in RL
  • Most used Reinforcement Learning algorithm (Q-learning)
  • Practical applications of Reinforcement Learning • Challenges with implementing RL

  Module 2: Markov Decision Processes and Bandit Algorithms

  Topics

  • Reinforcement Learning Problems
  • Formulating a basic Reinforcement Learning Problem
  • Framework for solving RL problem
  • Markov Processes
  • Markov Reward Processes
  • Markov Decision Processes
  • Bellman Equation
  • Bandit Algorithms (UCB, PAC, Median Elimination, Policy Gradient)
  • Contextual Bandits

  Module 3: Dynamic Programming & Temporal Difference Methods

  Topics

  • Introduction to Dynamic Programming
  • Policy valuation (Prediction)
  • Policy Improvement
  • Policy Iteration
  • Value Iteration
  • Generalized Policy Iteration
  • Asynchronous Dynamic Programming
  • Efficiency of Dynamic Programming
  • Temporal Difference Prediction
  • Why TD Prediction Methods
  • On-Policy and Off-Policy Learning Page
  • Q-learning
  • Reinforcement Learning in Continuous Spaces
  • SARSA

  Module 4: Value Function, Bellman Equation, Value Iteration, and Policy Gradient Methods

  Topics

  • Value Function
  • Bellman Equations
  • Optimal Value Functions
  • Bellman Optimality Equation
  • Optimality and Approximation
  • Value Iteration
  • Introduction to Policy-based Reinforcement Learning: Policy Gradient
  • Monte Carlo Policy Gradients
  • Generalized Advantage Estimation (GAE)
  • Monte Carlo Prediction
  • Monte Carlo Estimation of Action Values
  • Monte Carlo Control
  • Monte Carlo Control without Exploring Starts
  • Incremental Implementation •

  Policy optimization methods (Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO))

   

  NLP with Python

  Module 1: Introduction to Text Mining and NLP

  Topics:

  • Overview of Text Mining
  • Need of Text Mining
  • Natural Language Processing (NLP) in Text Mining
  • Applications of Text Mining
  • OS Module
  • Reading, Writing to text and word files
  • Setting the NLTK Environment
  • Accessing the NLTK Corpora

  Module 2: Extracting, Cleaning and Pre-processing Text

  Topics:

  • Tokenization
  • Frequency Distribution
  • Different Types of Tokenizers
  • Bigrams, Trigrams &Ngrams
  • Stemming
  • Lemmatization
  • Stopwords
  • POS Tagging
  • Named Entity Recognition

  Module 3: Analyzing Sentence Structure

  Topics:

  • Syntax Trees
  • Chunking
  • Chinking
  • Context Free Grammars (CFG)
  • Automating Text Paraphrasing

  Module 4: Text Classification-I

  Topics:

  • Machine Learning: Brush Up •

  Bag of Words

  • Count Vectorizer
  • Term Frequency (TF)
  • Inverse Document Frequency (IDF)

  Module 5: Text Classification-II

  Topics:

  • Converting text to features and labels
  • Multinomial Naiive Bayes Classifier
  • Leveraging Confusion Matrix

   

  AI & Deep Learning with TensorFlow

  Module 1 – Introduction to Deep Learning

  Topics:

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization

  Module 2: Understanding Fundamentals of Neural Networks with TensorFlow

  Topics:

  • How Deep Learning Works?
  • Activation Functions
  • Illustrate Perceptron
  • Training a Perceptron
  • Important Parameters of Perceptron
  • What is TensorFlow?
  • TensorFlow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step – Use-Case Implementation

  Module 3: Deep dive into Neural Networks with TensorFlow

  Topics:

  • Understand limitations of A Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • TensorBoard
  • Summary

  Master Deep Networks

  Topics:

  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation on SONAR dataset
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent
  • Types of Deep Networks

  Module 5: Convolutional Neural Networks (CNN)

  Topics:

  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks

  Module 6: Recurrent Neural Networks (RNN)

  Topics:

  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

  Module 7: Restricted Boltzmann Machine(RBM) and Autoencoders

  Topics:

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders

  Module 8: Keras

  Topics:

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

  Module 9 – TFLearn

  Topics:

  • Define TFLearn
  • Composing Models in TFLearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFLearn
  • Customizing the Training Process
  • Using TensorBoard with TFLearn
  • Use-Case Implementation with TFLearn

  Module 10 – PyTorch