Artificial Intelligence – simplisofttechnologies.com

AI & Machine Learning Curriculum

1. Python Programming

Introduction to Python
Introduction to Colab and Jupyter Notebook
Variables, data types, and basic operations
Control structures (loops, conditional statements)
Hands-on: Strings, lists, tuples
Python functions and built-in functions
Python lambda functions, generators
Higher-order functions (map, filter), list comprehension, file handling
Object-oriented programming in Python

2. Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
Applications of Machine Learning
Overview of Large Language Models (LLMs) and Generative AI
The Machine Learning Workflow
Key Libraries and Tools (NumPy, Pandas, scikit-learn)

3. Data Preprocessing

Data Cleaning and Handling Missing Values
Feature Engineering and Selection
Data Transformation and Scaling
Tokenization and preprocessing for LLMs
Label Encoding and Ordinal Encoding
Techniques for processing unstructured data for generative tasks

4. Supervised Learning Algorithms

Linear Regression: Understanding the Linear Regression model and its assumptions
Logistic Regression: Introduction to Logistic Regression for binary classification
Decision Trees and Random Forests: Understanding decision trees and their recursive nature
Support Vector Machines (SVM): Intuition behind the SVM algorithm
k-Nearest Neighbors (k-NN): Working principle and intuition
Naive Bayes: Probabilistic modeling with Bayes’ theorem
Gradient Boosting Machines (GBM): Concept of boosting and weak learners
Neural Networks and Deep Learning: Introduction to neural networks
Evaluation Metrics (Confusion Matrix, Accuracy, Precision, Recall, F1-score, ROC, AUC)

5. Model Evaluation and Selection

Train-Test Split and Cross-Validation (k-Fold, LOOCV)
Evaluation Metrics for Generative Models (BLEU, ROUGE, Perplexity)
Hyperparameter Tuning (Grid Search, Randomized Search)

6. Unsupervised Learning Algorithms

Introduction to clustering and its applications
k-Means and Hierarchical Clustering
Principal Component Analysis (PCA)
Singular Value Decomposition (SVD)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders and Variational Autoencoders (VAEs)
Introduction to Generative Adversarial Networks (GANs)

7. Feature Selection and Dimensionality Reduction

Motivation for feature selection
Types of feature selection: Filter, Wrapper, and Embedded methods
Feature importance and relevance
Using LLM embeddings for feature selection
Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD)

8. Ensemble Methods

Bagging and Random Forests
Boosting and AdaBoost
Stacking and Blending

9. Neural Networks and Deep Learning

Artificial Neural Networks (ANN)
Backpropagation Algorithm
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
Transformer Architecture: BERT, GPT, T5
Fine-Tuning Pre-trained Models
Prompt Engineering for LLMs

10. Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Stopword Removal, Lemmatization)
Text Representation: TF-IDF, Word Embeddings, and LLM-based embeddings
Sentiment Analysis
Named Entity Recognition (NER)
Text Classification (e.g., Spam Detection)
Applications of LLMs in text summarization and chatbot creation

11. LLMs and Generative AI (New Module)

Overview of LLMs: GPT, BERT, T5
Hands-on with OpenAI APIs or Hugging Face
Fine-tuning LLMs for specific tasks
Use cases of Generative AI (Text, Code, and Image Generation)
Introduction to Diffusion Models for image generation
Ethical considerations and challenges in Generative AI

12. Model Deployment and Productionization

Serialization and Deserialization of Models
Building APIs for Model Deployment
Dockerization
Deployment techniques for LLMs and Generative AI models
Cloud-based deployment: Azure and AWS
Creating real-time APIs for LLMs

13. Capstone Project: 40 Hours

Build and deploy an end-to-end solution leveraging LLMs or GenAI.
Example projects:
- Chatbot or text summarization with GPT models
- AI art generator using GANs or diffusion models
- Domain-specific fine-tuned LLM for business applications

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