100 Days of Machine Learning Code

This repository is dedicated to my 100-day journey of coding in machine learning. The goal of this challenge is to strengthen my understanding of machine learning concepts, algorithms, and applications through consistent coding practice. Each day, I will focus on a specific topic or project related to machine learning and write code to implement and explore it.

Motivation

The motivation behind this 100-day challenge is to deepen my knowledge and skills in machine learning. By dedicating focused time and effort each day, I aim to:

Enhance my understanding of fundamental machine learning algorithms and techniques.
Gain hands-on experience by implementing various machine learning models and projects.
Explore advanced topics in machine learning such as deep learning, reinforcement learning, and natural language processing.
Develop proficiency in popular machine learning frameworks and libraries, including TensorFlow, PyTorch, and scikit-learn.
Sharpen my problem-solving and coding abilities through consistent practice.

Repository Structure

Each day's code and related files will be organized in a separate folder, labeled with the corresponding day number.
The code files will be written in Python and will include detailed comments for clarity and explanation.
Any additional resources, such as datasets or pre-trained models, will be provided within each day's folder.
The README.md file within each folder will contain a brief description of the topic or project for that day.

Progress Tracking

I will TRY MY BEST 😅 to maintain a log of my daily progress in a separate document or in the main README.md file. This log will include a summary of the topic or project covered each day, challenges faced, lessons learned, and any notable achievements or insights gained. Additionally, I may include links to relevant articles, tutorials, or papers that I find helpful during my journey.

Contributions and Feedback

While this repository primarily serves as a personal coding challenge, I welcome contributions and feedback from the community. If you spot any errors in the code or have suggestions for improvements, please feel free to open an issue or submit a pull request. I appreciate any insights or guidance that can further enhance my learning experience.

Conclusion

I am excited to embark on this 100-day journey of coding in machine learning. I believe that consistent practice and exploration of diverse machine learning topics will broaden my skill set and deepen my understanding of this fascinating field. By the end of this challenge, I hope to have a comprehensive portfolio of machine learning projects and a stronger foundation in the principles and applications of machine learning.

Let's dive in and make the most of these 100 days of machine learning code!

Happy coding!!!

Name		Name	Last commit message	Last commit date
Latest commit History 139 Commits
Day 01 - Linear Regression		Day 01 - Linear Regression
Day 02 - Logistics Regression		Day 02 - Logistics Regression
Day 03 - K Nearest Neighbors		Day 03 - K Nearest Neighbors
Day 04 - Decision Trees		Day 04 - Decision Trees
Day 05 - Random Forest		Day 05 - Random Forest
Day 06 - Naive Bayes		Day 06 - Naive Bayes
Day 07 - Support Vector Machine		Day 07 - Support Vector Machine
Day 08 - Principal Component Analysis		Day 08 - Principal Component Analysis
Day 09 - K Means Clustering		Day 09 - K Means Clustering
Day 10 - Hierarchical Clustering		Day 10 - Hierarchical Clustering
Day 11 - Ensemble Method		Day 11 - Ensemble Method
Day 12 - Gradient Boosting Machines		Day 12 - Gradient Boosting Machines
Day 13 - XGBoost		Day 13 - XGBoost
Day 14 - Light GBM		Day 14 - Light GBM
Day 15 - Cat Boost		Day 15 - Cat Boost
Day 16 - Natural Language Processing (NLP)		Day 16 - Natural Language Processing (NLP)
Day 17 - Sentiment Analysis		Day 17 - Sentiment Analysis
Day 18 - Text Classification		Day 18 - Text Classification
Day 19 - Text Tokenization		Day 19 - Text Tokenization
Day 20 - Recurrent Neural Network (RNN)		Day 20 - Recurrent Neural Network (RNN)
Day 21 - Long Short Term Memory (LSTM)		Day 21 - Long Short Term Memory (LSTM)
Day 22 - Gated Recurrent Unit (GRU)		Day 22 - Gated Recurrent Unit (GRU)
Day 23 - Convolutional Neural Network (CNN)		Day 23 - Convolutional Neural Network (CNN)
Day 24 - Image Classification		Day 24 - Image Classification
Day 25 - Object Detection		Day 25 - Object Detection
Day 26 - Image Segmentation		Day 26 - Image Segmentation
Day 27 - Generative Adversarial Network (GAN)		Day 27 - Generative Adversarial Network (GAN)
Day 28 - Variational Autoencoders (VAEs)		Day 28 - Variational Autoencoders (VAEs)
Day 29 - Reinforcement Learning		Day 29 - Reinforcement Learning
Day 30 - Q - Learning		Day 30 - Q - Learning
Day 31 - Deep Q Networks (DQN)		Day 31 - Deep Q Networks (DQN)
Day 32 - Policy Gradients		Day 32 - Policy Gradients
Day 33 - Proximal Policy Optimization (PPO)		Day 33 - Proximal Policy Optimization (PPO)
Day 34 - Actor-Critic Methods		Day 34 - Actor-Critic Methods
Day 35 - Time Series Analysis		Day 35 - Time Series Analysis
Day 36 - Arima Models		Day 36 - Arima Models
Day 37 - Long Short Term Memory (LSTM)		Day 37 - Long Short Term Memory (LSTM)
Day 38 - Seasonal Decomposition for Time Series (STL)		Day 38 - Seasonal Decomposition for Time Series (STL)
Day 39 - Anomaly Detection		Day 39 - Anomaly Detection
Day 40 - Transfer Learning		Day 40 - Transfer Learning
Day 41 - Fine-tuning Pretrained Models		Day 41 - Fine-tuning Pretrained Models
Day 42 - Explainable AI		Day 42 - Explainable AI
Day 43 - Model Interpretability (SHAP, LIME)		Day 43 - Model Interpretability (SHAP, LIME)
Day 44 - Autoencoder		Day 44 - Autoencoder
Day 45 - Recommender Systems		Day 45 - Recommender Systems
Day 46 - Collaborative Filtering		Day 46 - Collaborative Filtering
Day 47 - Matrix Factorization		Day 47 - Matrix Factorization
Day 48 - Deep Generative Models		Day 48 - Deep Generative Models
Day 49 - Adverserial Attacks and Defenses		Day 49 - Adverserial Attacks and Defenses
Day 50 - Bayesian Optimization		Day 50 - Bayesian Optimization
Day 51 - Hyperparameter Tuning		Day 51 - Hyperparameter Tuning
Day 52 - Model Deployment with Flask		Day 52 - Model Deployment with Flask
Day 53 - Model Deployment with RestAPI		Day 53 - Model Deployment with RestAPI
Day 54 - Model Deployment with TensorFlow Serving		Day 54 - Model Deployment with TensorFlow Serving
Day 55 - Cloud-based Model Deployment (AWS)		Day 55 - Cloud-based Model Deployment (AWS)
Day 56 - Dockerizing Machine Learning Models		Day 56 - Dockerizing Machine Learning Models
Day 57 - Active Learning		Day 57 - Active Learning
Day 58 - Multivariate Regression		Day 58 - Multivariate Regression
Day 59 - One-Class Classification		Day 59 - One-Class Classification
Day 60 - Anomaly Detection in Network Traffic		Day 60 - Anomaly Detection in Network Traffic
Day 61 - Fraud Detection		Day 61 - Fraud Detection
Day 62 - Credit Scoring		Day 62 - Credit Scoring
Day 63 - Customer Segmentation		Day 63 - Customer Segmentation
Day 64 - AB Testing		Day 64 - AB Testing
Day 65 - Deep Reinforcement Learning		Day 65 - Deep Reinforcement Learning
Day 66 - Meta-Learning		Day 66 - Meta-Learning
Day 67 - Hyper Parameter Optimization of Neural Networks		Day 67 - Hyper Parameter Optimization of Neural Networks
Day 68 - Sequence-to-Sequence Models		Day 68 - Sequence-to-Sequence Models
Day 69 - Transformer Models		Day 69 - Transformer Models
Day 70 - Attention Mechanisms		Day 70 - Attention Mechanisms
.DS_Store		.DS_Store
README.md		README.md

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