🌟 Comprehensive AI/ML Resources📚 🌟

🚀 A Complete AI/ML Repository with in-depth coverage of Math, Python, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, and MLOps 🚀

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📑 Table of Contents

Section	Description
01. Math for AI and ML	Math foundations for AI/ML like linear algebra, calculus, statistic and probability.
02. Python	Python fundamentals, data structures, libraries, and more.
03. Machine Learning	Core ML algorithms, models, techniques, and much more.
04. Deep Learning	Dive into ANN, CNN, RNN, LSTM, and more.
05. NLP	Natural Language Processing using ML and DL models, preprocessing techniques, tools in NLP, and more.
06. Computer Vision	Introduction to CV, Object detection, GANs, image segmentation, and OpenCV.
07. Generative AI & LLMs	Explore the world of Large Language Models and Generative AI.
08. MLOps	Tools, courses, and resources for MLOps.

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01. Math for AI and ML 📐

1. Linear Algebra

   • Introduction, System of Equations, Matrix Operations, Eigenvalues & Eigenvectors, and more.

2. Calculus

   • Limits, Derivatives, Chain Rules, Partial Derivatives, etc.

3. Probability

   • Introduction to Probability, Bayes Theorem, Permutations, Probability Distribution, etc.

4. Statistics

   • Central Tendency, Hypothesis Testing, Plots (Box, QQ, Violin), and more.

Math Courses:

Platform	Links
🧑‍🏫 Khan Academy	Linear Algebra
🧑‍🏫 MIT	Linear Algebra
🧑‍🏫 3Blue1Brown	Linear Algebra
🧑‍🏫 3Blue1Brown	Calculas
🧑‍🏫 Coursera	Mathematics for Machine Learning and Data Science Specialization
🧑‍🏫 YouTube	Live Statistic (Krish Naik)

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02. Python 🐍

1. Python Basics

   • Variables, Data Types, Lists, Functions, OOP, Error Handling, etc.

2. Data Structures & Algorithms

   • Arrays, Sorting, Searching, Linked Lists, Trees, Graphs, and more.

3. NumPy | Pandas | Matplotlib | Seaborn

Python Courses:

Platform	Links
🐍 Udemy	Python Bootcamp (Jose)
🐍 Udemy	Python for Data Science (Jose)

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03. Machine Learning 🤖

📂 Topic	📑 Link to Notebook
1. Introduction To ML	🌐 Notebook
2. Linear Regression	🌐 Notebook
3. Logistic Regression	🌐 Notebook
4. Decision Tree	🌐 Notebook
5. SVM	🌐 Notebook
6. Naive Bayes	🌐 Notebook
7. KNN	🌐 Notebook
8. k-means Clustering	🌐 Notebook
9. Hierarchical Clustering	🌐 Notebook
10. DBSCAN	🌐 Notebook
11. PCA	🌐 Notebook
12. LDA	🌐 Notebook
13. Ensemble Learning	🌐 Notebook
14. Random Forest	🌐 Notebook
15. Gradient Boost	🌐 Notebook
16. XGBoost Regression	🌐 Notebook
17. XGBoost Classification	🌐 Notebook
18. Adaboost	🌐 Notebook
19. Regression Metrics	🌐 Notebook
20. Classification Metrics	🌐 Notebook
21. Lasso And Ridge Regression	🌐 Notebook
22. Hyperparameter Tuning & Cross Validation	🌐 Notebook
23. ML Project Life-cycle	🌐 Notebook

ML Courses:

Platform	Links
🤖 Coursera	Machine Learning Specialization (Andrew Ng)

| 🤖 YouTube | EDA Live (Krish Naik) | | 🤖 YouTube | EDA Playlist (Krish Naik) |

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04. Deep Learning 🧠

📂 Topic	📑 Link to Notebook
1. Introduction To DL	🌐 Notebook
2. ANN	🌐 Notebook
3. Activation Functions	🌐 Notebook
4. Loss Functions	🌐 Notebook
5. Optimization	🌐 Notebook
6. Vanishing Explodings	🌐 Notebook
7. Overfit And Uderfit	🌐 Notebook
8. CNN	🌐 Notebook
9. CNN Architectures	🌐 Notebook
10. RNN	🌐 Notebook
11. LSTM And GRU	🌐 Notebook
12. BRNN	🌐 Notebook
13. Tensorflow And PyTorch	🌐 Notebook

DL Courses:

Platform	Links
🧠 Coursera	Deep Learning Specialization (Andrew Ng)
🧠 Udemy	TensorFlow for Deep Learning Bootcamp
🧠 Udemy	PyTorch for Deep Learning Bootcamp

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05. Natural Language Processing (NLP) 💬

📂 Topic	📑 Link to Notebook
1. Introduction To NLP	🌐 Notebook
2. Word Embeddings	🌐 Notebook
3. Word2vec	🌐 Notebook
4. Seq2Seq	🌐 Notebook
5. Transformers	🌐 Notebook
6. DL Models In NLP	🌐 Notebook

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06. Computer Vision (CV) 👁️

📂 Topic	📑 Link to Notebook
1. Introduction To CV	🌐 Notebook
2. Object Detection	🌐 Notebook
3. OpenCV	🌐 Notebook
4. GAN	🌐 Notebook
5. Image Segmentation	🌐 Notebook

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07. Generative AI & LLMs 🤖📝

📂 Topic	📑 Link to Notebook
1. Introduction to Generative AI & LLMs	🌐 Notebook
2. Retrieval-Augmented Generation (RAG)	🌐 Notebook

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08. MLOps Resources ⚙️

1. Github: Github |

2. Docker: Docker Tutorial for Beginners (TechWorld with Nana) | End To End Machine Learning Project Implementation With Dockers (Krish Naik)

3. MLFlow: MLflow in Machine Learning

4. CI/CD: GitHub Actions Tutorial (TechWorld with Nana) | GitHub Actions (glich.stream)

5. Kubernete: Kubernetes Tutorial for Beginners (TechWorld with Nana)

6. AWS: AWS Cloud Practitioner (Udemy)

MLOps Courses:

Platform	Links
⚙️ Coursera	MLOps Specialization
⚙️ Udemy	Machine Learning Specialty

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Repository created and maintained by Fraidoon Omarzai

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