Data Science, AI & ML

Data science uses math and computer science to extract insights from data. AI is about creating machines that can do tasks that require human intelligence. ML is a subset of AI that involves teaching machines to learn from data. Together, these fields are transforming businesses by automating tasks, gaining insights, and making better decisions.

Data Science, AI & ML Course Roadmap

"The Journey to your future readiness begins here"

Getting Started

Psychometric Assessment to identify the right career path using your core strength. 1x1 Counselling

Choose the training/research track

Learn the basics of mathematics & Statistics

Design Thinking

Models to uplift you to think creatively and use ChatGPT type AI tools that can enrich your problem solving skills to be an impact maker at your job.

Tour into the world of Data Science

An End to End training on Data Science, Tools, Tracks, Mental models

Applied AI

Machine Learning In depth and hands on, EDA, Feature selection and engineering, Model building and Training

Research breakthroughs

Hands on support to make you perform break through researches Feasibility & ROI Analysis Market Research and Monetization

Profile Building & Ready to Market

Linked In, YouTube, CV support, Interview readiness

On the Job Support

Handholding support for first 2 months while on your job.

Applied AI

Just the ML you need

to make your presence felt in the world of data science!

About

AI and its related technologies have entered the mainstream of technological innovation. Thanks to the increased access to data, the increasing computational power and sophisticated sensors and algorithms. These technologies include machine learning, natural language processing, robotics and image processing.

This course is an introduction to AI from an applied perspective and has 30 hours of complete industry focused cutting-edge content covering Data Science, AI, Python for ML, Data Analytics and Machine Learning. Live sessions covering topics designed based current industry requirements to prepare students better for real-world problem-solving.

Fundaments of scalable Data Science – Foundational Course

The History and Evolution of Data.
How Data and Science are connected.
The Life Cycle of Data Science.
Data Analytics, Data Science & AI – The link..
Types of Data Analytics by Relevance.
Introduction to Big Data and the 4 V’s.

Introduction to Artificial Intelligence

Terminologies and differences between them – Artificial Intelligence, Machine Learning, Deep Learning.
History of AI (evolution of AI and its pace).
Proposing and evaluating real world AI applications
AI in the enterprise and AI in the future of work.

Fundamentals of Programming – Python for ML

Introduction to python
Working with Data.
The Life Cycle of Data Science.
Data Types & Variables
Making Decisions.
- Logical Expression
- Conditional statements
- Logical operators
- More complex expressions

Finding and Fixing problems
- Types of Errors
- Troubleshooting Tools
- Using the Python Debugger
Lists and Loops
- Lists and Tuples
- List Functions
- “For” & “While” Loops

Numeric and Date Functions
- Dates and Times
- Advanced Data Time Management
- Random Numbers
- The Math Library
Functions
- Writing and
- Calling Functions
- Function Inputs and Outputs
- Local and Global Scope

Machine Learning Basics

Types of Machine Learning – Supervised, Unsupervised and Reinforcement
Basic ML process
Classification and Regression Models.
Feature Engineering and deployment of ML Models.

Foundations of ML (Algorithms)

Linear Regression
Logistic Regression
Support Vector Machines
K-Nearest Neighbours
Decision Tree
Random Forest

ML Libraries – NumPy, Pandas, Matplotlib, Seaborn and Scikit-learn

NumPy (key operations)
Pandas (Series, DataFrame, key operations)
Matplotlib (basic plotting)
Seaborn (key plots)
Scikit-learn (key algorithms and operations)

Case Study 1 (end-to-end process) (2 hours)

ML Real World Case Studies.
Regression problem

Case Study 2 (end-to-end process) (2 hours)

Classification problem

Key Notes:

The entire course will be taught from an Industry stand point. Every session will have many practical examples and exercises that would help prepare our students better for real-world problem solving and interviews.
The validity of the Applied Machine Learning course is 90 days from the date of Enrollment.
Every live session will have many facts and figures to enable the latest happening in the industry.
More than 5 real world case studies (includes 2 Self Case studies).
12+ machine learning algorithms will be taught in this course.
After every learning milestones, there will be assignments and exercises to measure learners understanding of the subject and concepts
We will also be conducting 10+ hours of live content based on industry trends along with the research support.
Mentorship will be assigned to each candidate after the completion of 50% of the course assignments whose sole concentration would be on building the specific learner’s portfolio/resume and in interview preparation, mock interviews