🧠 Artificial Intelligence (AI)

Various Artificial Intelligence (AI) programs for solving problems, searching and pattern finding.

🚩 Difficulty Wise

1️⃣ Beginner Level

1. Python Programming for AI

• Topics: Syntax, data structures (lists, dictionaries), control flow, functions, NumPy (for numerical computing), Pandas (for data manipulation).
• Practice Ideas:
  • Build a simple calculator or a number guessing game.
  • Use Pandas to clean and analyze a dataset (e.g., Titanic dataset).
  • Use NumPy to perform matrix operations and transformations.

2. Mathematics Fundamentals

• Topics: Linear Algebra (vectors, matrices, dot products), Calculus (derivatives, gradients), and basic Statistics (mean, median, variance, standard deviation).
• Practice Ideas:
  • Implement a function to calculate the dot product of two vectors from scratch.
  • Manually calculate the gradient of a simple function like f(x) = x².

3. Introduction to Machine Learning

• Topics: What is ML? Difference between Supervised, Unsupervised, and Reinforcement Learning. The concept of training vs. testing data.
• Practice Ideas:
  • Use a library like scikit-learn to train a simple linear regression model to predict house prices based on square footage.

4. Core Supervised Learning Algorithms

• Topics: Linear Regression, Logistic Regression, k-Nearest Neighbors (k-NN), Decision Trees.
• Practice Ideas:
  • Linear/Logistic Regression: Predict student exam scores based on study hours.
  • k-NN: Build a classifier to identify different species of iris flowers.
  • Decision Trees: Classify whether a passenger on the Titanic survived or not.

5. Core Unsupervised Learning Algorithms

• Topics: k-Means Clustering, Principal Component Analysis (PCA).
• Practice Ideas:
  • k-Means: Segment customers of a mall based on their spending and demographic data.
  • PCA: Visualize a high-dimensional dataset (like the Iris dataset) in 2D.

6. Model Evaluation

• Topics: Train/Test Split, Cross-Validation, Evaluation Metrics (Accuracy, Precision, Recall, F1-Score for classification; Mean Squared Error for regression).
• Practice Ideas:
  • Train a model and evaluate it using 5-fold cross-validation, reporting multiple metrics.

2️⃣ Intermediate Level

1. Advanced Supervised Learning

• Topics: Support Vector Machines (SVM), Ensemble Methods (Random Forests, Gradient Boosting Machines like XGBoost, LightGBM).
• Practice Ideas:
  • Use a Random Forest or XGBoost model to win a Kaggle playground competition.
  • Compare the performance of SVM with a kernel against a simple logistic regression model on a non-linearly separable dataset.

2. Introduction to Neural Networks

• Topics: Perceptron, Multi-Layer Perceptron (MLP), Activation Functions (Sigmoid, Tanh, ReLU), Loss Functions, Backpropagation.
• Practice Ideas:
  • Build an MLP from scratch using only NumPy to solve the XOR problem.
  • Use a high-level framework like Keras/TensorFlow or PyTorch to build an MLP for classifying handwritten digits (MNIST dataset).

3. Introduction to Deep Learning for Computer Vision

• Topics: Convolutional Neural Networks (CNNs), layers (Conv2D, Pooling, Fully Connected), popular architectures (LeNet, AlexNet).
• Practice Ideas:
  • Build a CNN to classify images from the CIFAR-10 dataset.
  • Use Transfer Learning with a pre-trained model (like VGG16 or ResNet50) to classify your own set of images (e.g., cats vs. dogs).

4. Introduction to Deep Learning for NLP

• Topics: Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Recurrent Neural Networks (RNNs), LSTMs.
• Practice Ideas:
  • Train a simple sentiment analysis model (positive/negative review) using an LSTM on a dataset like IMDB reviews.
  • Use pre-trained GloVe embeddings as the input layer for your model.

5. Data Engineering for ML

• Topics: Feature Engineering, Handling Missing Data, Advanced Data Cleaning, Introduction to MLOps (ML + Operations) concepts.
• Practice Ideas:
  • Participate in a Kaggle competition and focus extensively on feature engineering to improve your model's score.
  • Build a pipeline that takes raw data, cleans it, and outputs a trained model.

3️⃣ Advanced Level

1. Advanced Deep Learning Architectures

• Topics: Transformers, Attention Mechanisms, BERT, GPT.
• Practice Ideas:
  • Fine-tune a pre-trained BERT model for a specific task like question answering or named entity recognition.
  • Implement a simple Transformer encoder from scratch (e.g., for translation).

2. Generative AI

• Topics: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models.
• Practice Ideas:
  • Train a DCGAN to generate realistic-looking faces (using a dataset like CelebA).
  • Use a pre-trained Stable Diffusion model to generate images from text prompts and experiment with fine-tuning.

3. Advanced Computer Vision

• Topics: Object Detection (YOLO, R-CNN), Image Segmentation (U-Net, Mask R-CNN), Image Generation (see above).
• Practice Ideas:
  • Implement an object detection model to identify and localize different objects in a webcam feed.
  • Use a U-Net architecture for a medical image segmentation task.

4. Advanced NLP & LLMs (Large Language Models)

• Topics: Prompt Engineering, LLM Fine-tuning (LoRA, QLoRA), RAG (Retrieval-Augmented Generation), AI Agent Frameworks.
• Practice Ideas:
  • Build a RAG system that uses your own documents (e.g., PDFs) to answer questions accurately.
  • Fine-tune a small LLM (like Gemma or Phi-3) on a specific style of writing or for a specific task.
  • Create a simple AI agent that can use tools (e.g., perform a web search, run code).

5. Reinforcement Learning (RL)

• Topics: Markov Decision Processes (MDPs), Q-Learning, Policy Gradients, Deep Q-Networks (DQN).
• Practice Ideas:
  • Implement Q-Learning to solve the FrozenLake or CartPole environment from OpenAI Gym.
  • Train a DQN agent to play a simple Atari game.

6. MLOps & Deployment

• Topics: Model Serving (TensorFlow Serving, TorchServe), Containerization (Docker), Orchestration (Kubernetes), CI/CD for ML, Model Monitoring.
• Practice Ideas:
  • "Dockerize" one of your intermediate models and create a simple API for it using FastAPI or Flask.
  • Deploy a model on a cloud service like AWS SageMaker, Google Cloud AI Platform, or Azure ML.

7. Specialized & Emerging Domains

• Topics:
  • Graph Neural Networks (GNNs): For social network analysis, recommendation systems.
  • Multimodal AI: Models that process both text and images (e.g., CLIP, GPT-4V).
  • AI Ethics & Explainable AI (XAI): Understanding and mitigating bias, interpreting model decisions.
  • AI for Science: Applying AI to problems in biology (AlphaFold), physics, and chemistry.

🎯 Topic Wise

1. Machine Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Feature Engineering and Selection
• Model Evaluation and Cross-Validation

2. Deep Learning

• Feedforward Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Long Short-Term Memory (LSTM) Networks
• Gated Recurrent Units (GRU)
• Transformers
• Autoencoders
• Attention Mechanisms
• Dropout, Batch Normalization, and Regularization

3. Computer Vision

• Image Classification
• Object Detection (YOLO, SSD, Faster R-CNN)
• Image Segmentation (U-Net, Mask R-CNN)
• Image Augmentation and Preprocessing
• Face Recognition
• Optical Character Recognition (OCR)
• Image Generation (GANs, Diffusion Models)

4. Natural Language Processing (NLP)

• Tokenization and Text Preprocessing
• Word Embeddings (Word2Vec, GloVe, FastText)
• Sequence-to-Sequence Models
• Sentiment Analysis
• Named Entity Recognition (NER)
• Text Classification
• Transformer Architectures (BERT, GPT, T5)
• Text Summarization
• Machine Translation
• Chatbot Development

5. Reinforcement Learning

• Q-Learning
• Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic Algorithms
• Proximal Policy Optimization (PPO)
• Monte Carlo Tree Search
• OpenAI Gym Practice Environments
• Multi-Agent Reinforcement Learning

6. Generative AI

• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Diffusion Models
• Text-to-Image Models
• Text Generation using LLM APIs
• Fine-Tuning and Prompt Engineering

7. MLOps and Model Deployment

• Model Serialization (Pickle, Joblib)
• Model Serving with FastAPI or Flask
• REST API Integration
• Dockerizing ML Models
• Continuous Training (CT) and Continuous Deployment (CD)
• Experiment Tracking (MLflow, Weights & Biases)
• Model Monitoring and Drift Detection

8. Data Engineering for AI

• Data Cleaning and Preprocessing (NumPy, Pandas)
• Feature Pipelines (Scikit-learn Pipelines)
• Handling Imbalanced Datasets
• Dimensionality Reduction (PCA, t-SNE, UMAP)
• Data Versioning (DVC)
• Dataset Augmentation for Vision/NLP

9. Advanced Deep Learning Architectures

• Vision Transformers (ViT)
• Graph Neural Networks (GNN)
• Siamese Networks
• Capsule Networks
• Self-Supervised Learning Architectures
• Multimodal Models (CLIP, BLIP)
• Diffusion Transformers (DiTs)

10. Optimization and Training Techniques

• Gradient Descent Variants (SGD, Adam, RMSProp)
• Learning Rate Scheduling
• Loss Functions Design
• Regularization (L1, L2, Dropout)
• Early Stopping
• Mixed Precision Training
• Hyperparameter Optimization (Optuna, Ray Tune)

11. Evaluation and Benchmarking

• Confusion Matrix, ROC-AUC, Precision/Recall
• BLEU, ROUGE, and METEOR (NLP metrics)
• Intersection over Union (IoU) for Vision
• F1 and Accuracy Scores
• Model Robustness and Adversarial Testing
• Cross-Validation and Bootstrapping

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🛠️ Tools and Frameworks

• Python (Undisputed main language in AI)
  • https://www.python.org/downloads/
• Python packages:

  pip install -r requirements.txt`

• Tools and Frameworks
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Keras
  • OpenCV
  • Hugging Face Transformers
  • ONNX
  • FastAI
  • LangChain (for LLM applications)
  • & more
• SQL (Data is the fuel for AI and SQL is the main language in data)
  • https://www.mysql.com/downloads/
• C/C++ (Performance-critical AI systems)
  • https://visualstudio.microsoft.com/vs/features/cplusplus/
• R (Statistical analysis and research)
  • https://cran.rstudio.com/
• Java (Enterprise AI systems)
  • https://www.java.com/en/
• Julia (New emerging language)
  • https://julialang.org/downloads/
• Prolog (language for simple relational statements)
  • https://www.swi-prolog.org/download/stable

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✍️ Endnote

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