AI Practitioner Associate
Master the foundations of AI development — from Python and data science to machine learning, deep learning, and introductory work with large language models.
Course Modules
Python Foundations for AI
Variables, data structures, functions, OOP, file I/O, and exception handling — the essential Python toolkit for AI development.
NumPy & Scientific Computing
Master array operations, linear algebra, broadcasting, and statistical functions for high-performance numerical computing.
Pandas & Data Manipulation
DataFrames, data loading, cleaning, filtering, grouping, and joining — complete data wrangling proficiency.
Data Visualization
Create compelling charts with Matplotlib, Seaborn, and Plotly. Master EDA techniques and data storytelling.
Machine Learning Fundamentals
Regression, classification, decision trees, random forests, and clustering — core ML algorithms explained from scratch.
Model Training & Evaluation
Train/test splits, classification and regression metrics, hyperparameter tuning, and overfitting prevention.
Introduction to Deep Learning
Neural networks, TensorFlow/Keras, CNNs, RNNs/LSTMs, and transfer learning — your gateway to deep learning.
Prompt Engineering
Zero-shot, few-shot, chain-of-thought, system prompts, output formatting, and advanced prompt patterns for LLMs.
LLM Fundamentals
How LLMs work, major model families, token economics, fine-tuning vs in-context learning, safety and limitations.
Introduction to RAG Pipelines
Document chunking, embeddings, vector stores, building a basic RAG pipeline, and evaluation techniques.