AI/ML Roadmap

1. Statistics

   • https://www.w3schools.com/ai/ai_statistics.asp
     • Min and Max
     • Variance
     • Deviation
     • Distribution
     • Skewness
     • Kurtosis
   • Probability
   • Hypothesis testing

2. Machine Learning (https://www.javatpoint.com/linear-regression-in-machine-learning)

   • Supervised Learning (https://www.geeksforgeeks.org/supervised-machine-learning)
     • Linear Regression
     • Logistic Regression
     • Decision trees
     • Random Forests
     • SVM
     • KNN
     • Naive Bayes
     • Gradient Boosting
   • Unsupervised Learning (https://www.geeksforgeeks.org/unsupervised-learning)
     • Clustering
       • K Means
       • Hierarchical Clustering
       • DBSCAN
     • Association Rule Learning
       • Apriori Algorithm
       • FP Growth Algorithm
     • Dimensionality Reduction
       • PCA
       • LDA

3. Deep Learning

   • CNN
   • RNN
   • LSTM
   • GAN
   • Transformer

4. NLP

   • Tokenization
   • POS
   • Embeddings
   • Lemmatization
   • Stemming

5. Generative AI

   • LLM (Text Generation)
     • Prompt Engineering
     • LLM Finetuning (LoRA)
     • RAG
       • Embedding Models
       • Vector DB
         • Pinecone - Fast and costly
         • Milvus - Scalable
         • Qdrant - Fast and Open Source
         • PG Vector
         • Datastax
         • FAISS - O.G
         • Chroma
       • DSPy
       • Langchain Framework
       • Llama Index Framework
       • Haystack Framework
     • AI Agents
       • Crew AI Agents Framework
       • Autogen Agents Framework
       • Smol Agents Framework
       • LangGraph Framework
     • LLM Inference (Deployment)
       • VLLM
       • TensorRT-LLM
   • GAN (Image Generation)
     • Comfy UI
     • Finetuning flux/diffusion models
   • Computer Vision
     • Open CV (Image Processing)
     • Deep Learning Frameworks (PyTorch/ Keras/ Tensorflow)
     • YOLO (Object Detection)
   • Predictive Modelling