DIPLOMA IN ARTIFICIAL INTELLIGENCE & MACHINE LEARNING – 1 YEAR

0 student Language: English Level: All levels 0 lesson Duration: 1 Year Location: Mandikatar, Miteripul

Description

Embark on an immersive journey into the transformative world of AI with our comprehensive course. Explore algorithms, neural networks, and machine learning, and discover their impact on industries like autonomous vehicles and personalized medicine. Gain hands-on experience with advanced tools and datasets, while examining the ethical implications of AI. Tailored for tech enthusiasts and creative thinkers alike, this course equips you with the skills to shape the future responsibly and intelligently.

Course Content

1-Year AI and ML Diploma (100 Weeks)

Total Duration: 50 weeks (2 hours/day, 6 days a week)

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)

Clustering: Centroid-based, Distribution-based, Density-based
Dimensionality Reduction: PCA, t-SNE, ICA

Hands-On: Customer segmentation, image compression

Weeks 19–20: Neural Networks and Optimization (12 sessions)

Architecture Basics: Layers, activation functions, loss functions
Optimization: Gradient descent, Adam optimizer
CNNs and RNNs

Hands-On: Train neural networks for tabular data classification

Weeks 21: Reinforcement Learning (6 sessions)

Reinforcement Learning Examples
Model-Based Methods vs Model-Free Methods

Weeks 22: SQL and Data Handling (6 sessions)

SQL Basics: Queries, joins, and integrating SQL with Python
Data Handling: Advanced cleaning, encoding, and scaling techniques

Hands-On: Use SQL for data analysis tasks

Weeks 23: Revision Exercises (6 sessions)

Basic Python Programming
NumPy and Pandas
Supervised and Unsupervised ML
SQL
Neural networks

Weeks 24–25: Capstone Project (12 sessions)

Real-World Problem: Collaborative projects integrating ML pipelines

Deliverables: Presentation, peer feedback

Weeks 26–28: R Programming and Hybrid Techniques (18 sessions)

Introduction to R: Data analysis and visualization basics
Combining Tools: Python and R in the same pipeline

Hands-On: Hybrid projects

Weeks 29–32: Advanced Machine Learning Techniques (24 sessions)

Ensemble Models: XGBoost, LightGBM
Time Series Analysis: ARIMA, LSTMs
AutoML Tools: H2O, Auto-sklearn
Hands-On: Build models for forecasting and anomaly detection

Weeks 33-34: Reinforcement Learning (12 sessions)

Model-Based Methods
Model-Free Methods (Value-Based, Policy-Based)

Weeks 35-36: Recommender Systems (12 sessions)

Techniques: Collaborative Filtering, Content-Based Filtering

Hands-On: Build a recommendation engine

Weeks 37–39: Computer Vision (18 sessions)

Image Processing: Preprocessing and augmentation
CNN Architectures: ResNet, EfficientNet
Applications: Object detection, segmentation
Hands-On: Train image classifiers

Weeks 40–42: Natural Language Processing (NLP) (18 sessions)

Text Processing: Tokenization, embeddings (Word2Vec, BERT)
Applications: Sentiment analysis, text summarization
Hands-On: Build chatbots and text classifiers

Weeks 43–44: Generative Models and Advanced AI Topics (12 sessions)

GANs: Generative Adversarial Networks
LLMs: Large Language Models, including fine-tuning

Hands-On: Build and experiment with GANs/LLMs

Weeks 45: AI Ethics (6 sessions)

Ethics: Bias, transparency, fairness

Weeks 46-47: Deployment (12 sessions)

Deployment: Flask/Django, cloud hosting
Hands-On: Deploy ML models on AWS/GCP

Weeks 48–50: Capstone Project and Presentation (18 sessions)

Comprehensive AI system development (healthcare, finance, or other domains)
Project milestones, reviews, and presentations

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

Duration 1 Year
Activities AI and ML

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DIPLOMA IN ARTIFICIAL INTELLIGENCE & MACHINE LEARNING – 1 YEAR

Course Content

1-Year AI and ML Diploma (100 Weeks)

Software Covered

Career Opportunities in AI & ML

Senior AI Engineer

AI Research Scientist

Chief AI Architect

Data Scientist

Machine Learning

Our Recruiters

Student Inquiry

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Related Courses

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DIPLOMA IN ARTIFICIAL INTELLIGENCE & MACHINE LEARNING – 1 YEAR

Course Content

1-Year AI and ML Diploma (100 Weeks)

Software Covered

Career Opportunities in AI & ML

Senior AI Engineer

AI Research Scientist

Chief AI Architect

Data Scientist

Machine Learning

Our Recruiters

Student Inquiry

Course Features

Related Courses

Quick Links

Links​

Social Media

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