Overview

This foundational course introduces learners to the world of Artificial Intelligence and Machine Learning through real-world examples, essential concepts, and beginner-friendly tools. Students gain a broad understanding of how machines learn, how data is processed, and how AI is transforming industries.

Who Should Attend

• Students and fresh graduates
• Non-technical professionals exploring AI/ML
• IT beginners and support staff
• Early-career developers or analysts

Key Learnings

• Understand the differences between AI, ML, and Data Science
• Learn data types, variables, logic, and decision structures
• Introduction to Python programming
• Basics of data wrangling and visualization
• Exposure to AI applications in daily life

Skills Attained

• Write simple Python programs for data analysis
• Perform basic data cleaning and plotting
• Understand AI terminology: supervised, unsupervised learning, classification
• Describe use cases in finance, healthcare, security, etc.

Course Modules

• Introduction to AI, ML, and Data Science
• Basics of Python Programming
• Working with Data: CSV, Excel, JSON
• Data Cleaning with Pandas
• Visualization with Matplotlib and Seaborn
• Introduction to Algorithms & Models
• AI/ML in the Real World
• Beginner Mini Project

Certification

Participants will receive the AI/ML Level 1 – Fundamentals Certificate, demonstrating foundational competence in data and AI technologies.

Overview

This level focuses on building and deploying machine learning models using industry-standard tools and methods. It emphasizes applied learning through structured datasets, algorithm selection, and performance evaluation.

Who Should Attend

• Junior data analysts or software engineers
• Students with basic Python and math skills
• IT professionals aiming to pivot to AI roles

Key Learnings

• Learn statistical foundations required for ML
• Explore supervised and unsupervised ML algorithms
• Apply Scikit-learn to train and tune models
• Understand the ML pipeline: preprocessing to evaluation
• Perform real-world model building and analysis

Skills Attained

• Apply regression, classification, clustering algorithms
• Evaluate models using confusion matrix, accuracy, precision, recall
• Automate data transformation and feature engineering
• Work with Jupyter notebooks and Scikit-learn
• Apply ML to business datasets

Course Modules

• Linear Algebra & Statistics for ML
• Supervised Learning: Linear, Logistic Regression
• Decision Trees, Random Forests, KNN
• Unsupervised Learning: Clustering, PCA
• Model Tuning & Evaluation
• Working with Real-World Datasets
• Introduction to Model Deployment
• Capstone Project: ML Use Case

Certification

Participants will receive the AI/ML Level 2 – Practitioner Certificate, showcasing applied ML modeling proficiency.

Overview

The advanced program is designed for professionals aiming to become AI/ML specialists or transition into data science, NLP, or AI engineering roles. The focus is on deep learning, neural networks, NLP, computer vision, and Generative AI.

Who Should Attend

• Working professionals in AI/ML or data science
• AI/ML Level 2 graduates
• Developers transitioning into AI engineer roles
• Individuals preparing for AI certifications or interviews

Key Learnings

• Build deep learning models using TensorFlow &Keras
• Apply Convolutional Neural Networks (CNNs) for image classification
• Work with LSTM, transformers, and NLP models
• Build and fine-tune large language models (LLMs)
• Explore ethical AI, responsible AI, and bias reduction

Skills Attained

• Implement DNNs, CNNs, and RNNs for different datasets
• Train AI systems for text, speech, and image processing
• Build end-to-end AI pipelines with deployment options
• Apply Prompt Engineering and work with Generative AI
• Create AI-driven projects for business/enterprise scenarios

Course Modules

• Deep Learning Overview &Keras/TensorFlow Basics
• Neural Networks (Feedforward, Backpropagation)
• CNNs for Image Recognition
• RNNs, LSTMs for Time-Series & NLP
• Transformers & BERT Models
• Prompt Engineering & Generative AI
• Responsible AI & Bias Mitigation
• Final Project & Interview Prep

Certification

Participants will receive the AI/ML Level 3 – Advanced Role-Based Certificate, validating high-end skills for job roles like AI Engineer, Data Scientist, or ML Researcher.