Candidates with a strong interest in AI and Machine Learning and have a desire to apply AI skills. No prior knowledge of AI or Machine Learning is required, but a solid foundation in using Python programming language is required.

• Must have minimum 1 year Python programming language experience
• Understanding of mathematics/statistics will be beneficial.

Course Outline:

Introduction to AI

1. History of AI
2. AI Terminology: Machine Learning, Deep Learning

Pandas, Numpy Libraries

1. Using Numpy

• Working with Numpy API
• Numpy Assignment

2. Working with Pandas

• Use Pandas library for data preparation
• Performing Exploratory Data Analysis
• Pandas Assignment

Machine Learning Fundamentals: 

1. Understanding Machine Learning

• How different is Machine Learning development compared to normal software development
• Understanding Machine Learning Techniques
• Understand steps involved in machine learning

2. Performing Classification

• Understand what classification is?
• Build classification models using different Algorithms
• Use classification metrics to evaluate models
• Working with unbalanced labels
• Multiple hands-on

3. Performing Regression

• Working with regression problems
• Build regression models using appropriate algorithms
• Understand regression metrics for model evaluation
• Multiple hands-on

4. Unsupervised Learning

• What is unsupervised learning?
• Understanding the intuition behind K-Means Clustering
• Build K-Means and Hierarchical clustering models
• Multiple hands-on

5. Model Optimization

• Understanding model tuning & hyper parameters
• Working with K-Fold cross validation
• Using Grid Search to select hyper parameters
• Using box plot to compare model performance
• Multiple hands-on

6. Assignment

• Build model(s) using given data

Deep Learning: 

1. Tensors using TensorFlow

• Understand what tensors are
• What are Keras and TensorFlow libraries?
• Creating different types of tensors using TensorFlow library
• Hands-on with tensors

2. Neural Networks

• Understanding Neural Networks and the intuition behind them
• Gradient Descent and Back Propagation
• Working with Activations functions
• Loss functions for different problems
• What are optimizers and how do they fit in neural networks?
• Multiple hands-on

3. Working with Images

• Working with images and image augmentation
• Performing image classification using neural networks
• Limitations of neural networks
• Understanding Convolutional Neural Networks (CNN) architecture
• Working with different Keras layers & sequential API for image classification
• Multiple hands-on

4. Transfer Learning

• Understanding transfer learning and its benefits
• Different types of transfer learning options
• Transfer learning for image and text classification
• Working with Keras Functional API to perform transfer learning
• Model Deployment using Gardio
• Multiple Hands-on with transfer learning to perform image classification

5. Natural Language Processing (NLP)

• What is NLP? And what are the use-cases
• NLP processing and different techniques to process text
• Working with Word Embedding – generating & visualizing
• Exploring different libraries in NLP processing
• Multiple Hands-on with NLP for text classification

6. Recurrent Neural Networks (RNN)

• What are Recurrent Neural Networks?
• Challenges with RNNs
• RNN different layers: LSTM & GRU
• Hands-on predict stock trend using RNN

7. Transformers

• Understanding Transformer Architecture
• Working with Attention, Self-Attention, Positional Encoding
• Using Transformers in NLP
• Hands-on working with transformers

Generative AI: 

1. Understanding Generative AI

• Understand what Generative AI is
• What are Large Language Models and how to use them

1. Working with LangChain

• What is Python framework “LangChain” and what are its benefits
• Model inputs and model outputs
• Working with data connections
• Using chains and agents
• Multiple hands-on on using LangChain

Capstone Project

• Project Overview
• Complete projects to get experience and practice
• Industry Use Case Studies

Training Material Provided

• Digital format
• Hands-on Lab: Students will use “Google Colaboratory”, hence students must have a Google Account