AI and ML – 6 Months Certification

0 student Language: English Level: All levels 0 lesson Duration: Lifetime access Location: Mandikatar, Miteripul

Description

This 6-month certification dives deeper into AI and ML, introducing supervised and unsupervised learning, computer vision, and natural language processing (NLP). Students will gain hands-on experience by working on real-time projects and deploying machine learning models.

Course Content

6 Months AI and ML Diploma (100 Weeks)

Total Duration: 25 weeks (6 days a week, 2 hours per session)

Week 1–2: Introduction to Programming with Python (12 sessions)

Basics of programming: Variables, data types, and operators
Conditional statements and loops (if-else, for, while)
Functions: Definition, arguments, and return values
Introduction to Python IDEs and Jupyter Notebooks
Hands-on: Simple programs like calculators, and condition-based programs

Week 3: Python Data Structures (6 sessions)

Lists, tuples, and dictionaries: Creation, manipulation, and iteration
Strings: Common operations and slicing
Sets: Use cases and applications
Hands-on: Build small projects like contact books or inventory systems

Week 4: Python Libraries and File Handling (6 sessions)

Introduction to Python libraries (NumPy, Pandas, and Matplotlib)
File handling: Reading/writing text and CSV files
Hands-on: Process and visualize simple datasets

Weeks 5–6: Exploratory Data Analysis (12 sessions)

Pandas: Data manipulation, cleaning, and aggregation
Matplotlib & Seaborn: Data visualization techniques

Hands-On: Analyze datasets (e.g., sales or weather data)

Weeks 7–8: Mathematics for AI and ML (12 sessions)

Linear Algebra: Vectors, matrices, and transformations
Probability: Distributions, Bayes’ theorem, and expectations
Calculus: Derivatives and optimization basics
Hands-On: Solve mathematical problems using Python

Weeks 9–10: Introduction to Machine Learning (18 sessions)

ML Basics: Supervised vs. unsupervised learning
Regression: Linear regression (theory and implementation)
Classification: Logistic regression, decision trees, and random forests
Unsupervised: Clustering (K-Means), Dimensionality Reduction

Hands-On: Predict house prices, classify spam emails

Week 11: Feature Engineering and Preprocessing (6 sessions)

Handling missing data
Feature scaling and normalization
Encoding categorical variables
Hands-on: Prepare a dataset for ML models

Week 12: Basics of Reinforcement Learning and Deep Learning (6 sessions)

Reinforcement Learning Overview

Neural Networks: Basic structure and functioning

Introduction to TensorFlow/Keras

Weeks 13: Revision Exercises (6 sessions)

Data Structures
Statements and Loops
Functions
NumPy and Pandas
Supervised and Unsupervised ML

Weeks 13–14: Final Projects (6 sessions)

Project: Exploratory Data Analysis of any tabular data
Project: Develop an end-to-end ML solution (e.g., sales prediction)

Weeks 15–16: Advanced Supervised Learning (12 sessions)

Regularization: Ridge and Lasso regression
Advanced Classification: SVM, ensemble methods (Random Forest, Gradient Boosting), k-NN, Naïve Bayes, Gradient Boosting
Hands-On: Build robust supervised models

Weeks 17–18: Unsupervised Learning and Clustering (12 sessions)