Course Overview

Machine learning has become the core of the value creation and transformation process in all spheres of our life - Business, Agriculture, Healthcare, Government,Defence and Research. The race to gain competitive advantage through machine learning has created a big gap between demand and availability of qualified people in this field.

The machine learning course from Sudaksha prepares the participants to fill this skill gap. Participants are trained the following platforms

• Python
• scikit-learn
• TensorFlow

They learn how to solve real life problems through exercises, hackathons and projects. The objective of the course is to make the participants ready for deployment on customer projects in various domains.

Faculty

Vinay Kumar PGDM, IIM Ahmedabad Leadership roles in IT services and product companies

Vinay has deep experience in designing, building, testing and implementing software products in manufacturing, education and BFSI domains. Vinay has worked as consultant to many global banks in US and regional banks in south east Asia. He has created several applications using machine learning and is an accomplished Trainer of Machine Learning and Deep Learning.

The training program designed by him lays emphasis on practical aspects of the machine learning lifecycle so that participants get ready to work on customer projects/ products immediately after the training.

Participant Profile

This course is designed for the following three groups of learners

• Those who are already employed but are exploring how to make a switch to a career in Artificial Intelligence and Machine Learning and uplift your career progression
• People in various roles such as domain experts, consultants and business leaders who want to understand how machine learning can be applied in their areas of work - for their own company or for their customers
• Fresher’s who want to make themselves more valuable for the prospective employers by having expertise in building applications with Artificial Intelligence and Machine Learning

Learning Path

AIML Foundation Module

• Overview of AIML
• Fundamentals of Machine Learning
• Lifecycle of creating a solution
• Performance Metrics
• What organisation need to prepare and change
• Case Study/ Exercise

Machine Learning Expertise Module

• Understanding the current landscape of Machine Learning
• Creating a prediction solution with Regression
• Various types of Regression
• Data Processing
• Feature Selection and Feature Engineering
• Model Selection and Model validation
• Different types of algorithms
• Applying different algorithms on Regression problem
• Training the model
• Evaluating the Model
• Making sense of Performance Metrics - Regression
• Regularisation/ Hyperparameter tuning
• Classification - Applying the framework for classification
• Performance Metrics - Classification
• Unsupervised Learning - Clustering
• Performance Metrics - Clustering
• Unsupervised Learning - Other application
• Time series modelling
• Refining the solution - Ensemble methods
• Natural Language Processing

The Deep Learning EDGE

• OVER VIEW OF Deep Learning
• Types of Learning
• Types of Networks
• Deep Learning Architecture
• Backpropagation
• Introduction to Tensorflow and Keras
• Types of Layers
• Built-in Ops
• Solving Regression and Classification problems with Neural Networks
• Refining the Solution
• Restricted Boltzmann Machine (RMB)
• Natural Language Processing
• Computer Vision
• Autoencoders
• Generative Adversarial Network

Benefits - key Learning Outcomes

Knowledge of Machine Learning and Deep Learning - World-class curriculum designed by experienced professionals

Mastering the tools used in Machine Learning and Deep Learning - Python, Scikit-Learn and Tensoflow

Understanding how to formulate problems that can be solved using Machine learning

Skills in designing Neural Networks & selection of appropriate algorithms for specific problems in various areas such as Computer Vision, Natural Language Processing, Optimisation and Predictions

Ability to embed Machine Learning algorithms into enterprise and consumer applications - brings you closer to the expectation of prospective employers

Put your career on faster path of growth

Differentiation & Career Services

Personal involvement of the trainers on the subject

Learning enabled by practical exercises and projects

To make sure that participants ready to deliver hands-on the customer projects

Career Mentorship

Resume Building

Mock Interviews

Hackathons

Admission & Fees

Prerequisite

Prerequisites for the course include basic understanding of calculus, linear Algebra and probability. Prior coding experience in Python is helpful but not necessary.

Admission & Fees

You could register for either of the modules or for the entire Master Course which includes all the three modules.

Fees

Master Course on AIML (includes all three modules) - 24999/-

Duration

Application Timeline

The Registration of Courses are open in each quarter. For the registration of current quarter are opened (Dec 2020 - Feb 2021) until 30th of NOV