🚀 The Ultimate Roadmap to Mastering AI, ML, DL & DP!

Artificial Intelligence (AI) Roadmap

1. Mathematics for AI

• Linear Algebra (Vectors, Matrices, Eigenvalues)

• Calculus (Derivatives, Partial Derivatives, Chain Rule)

• Probability & Statistics (Bayes’ Theorem, Distributions)

• Optimization (Gradient Descent, Lagrange Multipliers)

2. Machine Learning (ML)

• Supervised Learning (Regression, Classification)

• Unsupervised Learning (Clustering, Dimensionality Reduction)

• Reinforcement Learning (Q-learning, Deep Q Networks)

• Feature Engineering & Model Evaluation

3. Deep Learning (DL)

• Neural Networks (Perceptron, Backpropagation)

• CNNs, RNNs, Transformers

• Autoencoders, GANs

• Fine-Tuning & Transfer Learning

4. Practical AI Applications

• AI in NLP, Computer Vision, and Recommendation Systems

• Model Deployment & Optimization

• Ethical AI & Bias in AI

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Dynamic Programming (DP) Roadmap

1. Fundamentals

• Recursion vs. DP

• Memoization & Tabulation

• Overlapping Subproblems & Optimal Substructure

2. Classical DP Problems

• Fibonacci, Coin Change, Knapsack

• Longest Common Subsequence, Longest Increasing Subsequence

• Matrix Chain Multiplication, Partition Problems

3. Advanced DP

• DP on Trees

• DP with Bitmasking

• DP on Graphs (Dijkstra, Bellman-Ford)

• State Space Reduction & Bit DP

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