Artificial Intelligence & Machine Learning Course

Unlock the Power of AI: Master Machine Learning & Build Intelligent Systems

Duration

3 Months

Intensive

Live

Online

Intensive

Format

Hybrid

Hands-on, Practical

Cohort

Starting

Soon !!!

Program Details

Dive into the world of Artificial Intelligence and Machine Learning (AI/ML) in this pre-graduation program! This program equips you with the in-demand skills to excel in this rapidly growing field. Master the Python programming language, the foundation for AI/ML, and leverage powerful libraries like TensorFlow to build and train your own machine learning models. Go beyond theory with practical applications: create compelling data visualizations using Matplotlib and Seaborn. Explore the cutting edge: delve into fundamental algorithms like clustering and classification, and venture into exciting fields like Natural Language Processing (NLP) and Large Language Models (LLMs). But that's not all! Gain invaluable real-world experience through 10+ live industry projects and solidify your knowledge with 20+ hands-on assessments. Don't just learn AI/ML – build the future with it! Enroll today and unlock a world of possibilities.

Who should enroll?

-You have a keen interest in Artificial Intelligence, Machine Learning, or Deep Learning.
-You want to explore the fundamentals and advanced concepts of AI and ML.
-You want to build a strong foundation to support your academic and career aspirations.
-You are looking to transition into the field of AI and ML from another domain.
-You want a comprehensive program to kickstart your new career path.

Join our community to learn, connect with like-minded peers, and get updates on the scholarship test.

Limited Seats in the Cohort !

Teaching plan

Session 1:Introduction to AI & Machine Learning

Session 2: Fundamentals of python-I

Session 3: Fundamentals of python-II

Session 4: Fundamentals of python-III

Session 5: Introduction to Numpy

Session 6: Introduction to Pandas

Session 7: Data Visualisation in Python

Session 10: Introduction to Numpy

Session 11: Hypothesis Testing

Session 8: Exploratory Data Analysis

Session 9: Exploratory Data Analysis- Assignment

Machine Learning-I

Session 12: Regression line

Session 12(i): Assumptions of linear Regression

Session 13: Simple Linear Regression in Python Using statsmodels

Session 14: Simple Linear Regression in Python Using sklearn

Session 15: Multiple Linear Regression Theory

Session 16: Multiple Linear Regression in Python

Session 17: Logistic Regression Theory

Machine Learning-II (Not Covered in this module)

Session 18: Naive Bayes Theory

Session 26-36: Boosting => Clustering=>PCA=>Assignment

Session 22-25: Tree Models : Decision Tree/Random Forest

Session 19-21: Advanced Regression