8449048d-6e77-4bc0-adbb-e162b0b4ca75

AI and ML – 4 Months Certification

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

Description

This 3.5-month certification is designed for beginners who want to start their journey in Artificial Intelligence and Machine Learning. The course focuses on fundamental AI concepts, machine learning algorithms, and data handling techniques. By the end of the program, students will be able to develop basic AI models and understand real-world applications.

Course Content

4 Months AI and ML Diploma (100 Weeks)

Total Duration: 14 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)

Bonus

Gen AI Foundations & Prompt Engineering Techniques
Creative Writing & Idea Generation using Gen AI
Visual Storytelling with Gen AI
Marketing Innovation with Gen AI
Dynamic Design & Prototyping with Gen AI
Video Content Creation with Gen AI
Audio Innovation with Gen AI
3D Model Creation
Responsible Usage & Ethical Consideration in AI
Project

Software Covered

Matplotlib

TensorFlow

PyTorch

NumPy

Pandas

Jupyter

Scikit Learn

Python

Career Opportunities in AI & ML

After completing the AI & ML Diploma program, you can explore roles such as:

Senior AI Engineer

AI Research Scientist

Chief AI Architect

Data Scientist

Machine Learning

Our Recruiters

Some of the leading studios & companies that have recruited MAAC students include:

Student Inquiry

Reach out to us today and take the first step toward your creative journey with IEC Analytics.

Course Features

Activities AI and ML

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