Apply online through this link : https://tinyurl.com/mpm8j3em
OBJECTIVES OF TRAINING
To provide students with fundamental knowledge and practical exposure to Artificial Intelligence.
To foster collaboration between i-HUB DivyaSampark and BITS-Pilani.
To enhance visibility and brand endorsement of i-HUB DivyaSampark in academic and training domains
CERTIFICATION
Students will receive a Certificate of Completion from iHUB DivyaSampark, IIT Roorkee,
Recognized for academic and professional advancement
PROGRAM DETAILS
Duration of training: 40 hours (5 days)
Mode of Delivery: Offline (on-campus at BITS-Pilani)
Tentative Date: Third Week of December
Day 1: Introduction to Python for Machine Learning (7 Hours)
Overview of Machine Learning & AI
Python Basics: Variables, Data Types, Control
y Structures
Development Environment Setup: Jupyter Notebook & PyCharm
Core Python Programming: Loops, Functions, and Modules
NumPy Fundamentals: Arrays, Indexing, Slicing, Basic Operations
Pandas Essentials: Series, DataFrames, Import/Export (CSV, Excel, JSON)
Interactive Session: MCQs and Hands-on Practice
Day 2: Understanding Data & Feature Engineering (7 Hours)
Data Handling with Pandas: Missing Values, Filtering, Indexing
Data Cleaning & Transformation: Managing Imbalanced Datasets, Duplicates
Feature Engineering Techniques: Normalization, Min-Max Scaling, Encoding
Data Integration: Merging, Joining, Concatenating DataFrames
Interactive Session: MCQs and Hands-on Practice
Day 3 & 4: Data Visualization & Supervised Machine Learning (7 Hours Each)
Introduction to Data Visualization with Matplotlib
Line, Bar, and Scatter Plots
Plot Customization (Titles, Labels, Legends)
Advanced Visualization with MATLAB
Regression Techniques:
Simple Linear Regression
Multiple Linear Regression
Classification Algorithms:
Logistic Regression
k-Nearest Neighbours (KNN)
Interactive Session: MCQs and Hands-on Practice
Day 5 & 6: Model Analysis, Optimization & Project Work (7 Hours Each)
Model Evaluation Techniques: Confusion Matrix, Precision, Recall, F1-score
Decision Tree Classifier
Support Vector Machines (SVM) & Naïve Bayes
Unsupervised Learning Methods:
K-Means Clustering
Hierarchical Clustering
Introduction to Deep Learning:
Basics of Neural Networks
Neural Network Implementation using Python
Capstone Project: Real-world Case Study & Model Development