Welcome to my 30 Days ML/AI Challenge repository! This journey is all about diving deep into Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL). Over the next 30 days, I’ll be learning, implementing, and documenting various ML/AI concepts and projects.
The repository is organized into 30 folders, one for each day of the challenge. Each folder contains:
- README.md: Documentation for the day's tasks, learnings, and resources.
- Code/Projects: Implementation files and notebooks for the day's topic or project.
- Master fundamental and advanced ML/AI concepts.
- Gain hands-on experience by working on real-world projects.
- Build a solid foundation in Deep Learning with frameworks like TensorFlow and PyTorch.
- Document everything to ensure a comprehensive learning process.
- Day 01: Introduction to ML/AI
- Day 02: Linear Regression and Gradient Descent
- Day 03: Logistic Regression and Classification
- Day 04: Data Preprocessing and Feature Engineering
- Day 05: Exploratory Data Analysis (EDA)
- Day 06: Decision Trees and Random Forests
- Day 07: Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- Day 08: Support Vector Machines (SVM)
- Day 09: Clustering (K-Means, Hierarchical)
- Day 10: Dimensionality Reduction (PCA, t-SNE)
- Day 11: Ensemble Methods (Bagging, Boosting, Stacking)
- Day 12: Hyperparameter Tuning
- Day 13: Recommender Systems
- Day 14: Time Series Analysis
- Day 15: Introduction to Deep Learning
- Day 16: Neural Networks and Backpropagation
- Day 17: Convolutional Neural Networks (CNNs)
- Day 18: Recurrent Neural Networks (RNNs)
- Day 19: Natural Language Processing (NLP) Basics
- Day 20: Sentiment Analysis Project
- Day 21: Transfer Learning
- Day 22: Generative Adversarial Networks (GANs)
- Day 23: Reinforcement Learning Basics
- Day 24: Chatbots and Sequence Models
- Day 25: AI Ethics and Responsible AI
- Day 26: Deployment of ML Models (Flask, FastAPI)
- Day 27: Full ML Pipeline Project
- Day 28: Final DL Project: Image Classification
- Day 29: Final NLP Project: Text Generation
- Day 30: Summary and Future Roadmap
- Programming: Python
- Libraries/Frameworks: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, Matplotlib, Seaborn
- Tools: Jupyter Notebook, Google Colab, VS Code
- Deployment: Flask, FastAPI, Streamlit
Feel free to fork this repository, suggest improvements, or share feedback! Collaboration is always welcome.
Here are some resources I’ll use during this challenge:
- Books:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- Deep Learning by Ian Goodfellow
- Courses:
- Websites:
Let’s grow together in this journey. Connect with me: