AIML Training
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
AIML Foundation |
3 weeks |
Machine Learning Expert |
6 weeks |
Deep Learning Edge |
6 Weeks |
Master Course on AIML
(Includes all three modules) |
15 weeks |
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