AI & ML Hands-on Workshop

About Course

This workshop is designed to help learners strengthen the concepts of AI & ML through experiential learning as this is a practice-oriented workshop. It consists of exercises which will help to learn the relevant methods and practices around machine learning while focusing on artificial neural networks, the basis for deep learning

Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling.
Perform scientific and technical computing using the SciPy package and its sub-packages, such as Integrate, Optimize, Statistics, IO, and Weave.
Build deep learning models on the cloud using AWS Sagemaker.
Master advanced topics, such as Keras and TensorFlow, elements of a Keras model, Keras on GPU etc.

Course Content

Python for Data Science

Python Basics
Python Data Structures
Python Programming Fundamentals
Working with Data in Python
Working with NumPy Array

About Course

What Will You Learn?

Course Content

Python for Data Science

Python Basics

Python Data Structures

Python Programming Fundamentals

Working with Data in Python

Working with NumPy Array

Applied Data Science with Python

Data Science Overview

Data Analytics Overview

Statistical Analysis and Business Applications

Python Environment Setup and Essentials

Mathematical Computing with Python (NumPy)

Scientific Computing with Python (SciPy)

Data Manipulation with Pandas

Data Visualization in Python using Matplotlib

Machine Learning

Introduction to Artificial Intelligence and Machine Learning

Data Pre-Processing

Supervised Learning

Feature Engineering

Supervised Learning Classification

Unsupervised Learning

Time Series Modelling

Ensemble Learning

Recommender Systems

Deep Learning with TensorFlow and Keras

AI & Deep Learning Introduction

Artificial Neural Network

Deep Neural Network and Tools

Deep Neural Net Optimization, Tuning, and Interpretability

Convolutional Neural Net (CNN)

Recurrent Neural Networks

Autoencoders

Advanced Deep Learning and Computer Vision

RBM and DBNs

Variational Autoencoder

Working with Deep Generative Models

Applications: Neural Style Transfer and Object Detection

Distributed & Parallel Computing for Deep Learning Models

Reinforcement Learning

Deploying Deep Learning Models and Beyond