100 days of Machine Learning

In this post, I have curated a 100-day machine learning roadmap that includes hands-on projects and relevant learning resources. It's important to note that the timeline for landing a job in the machine learning field can vary depending on individual efforts and external factors, but this roadmap will give you a solid foundation to work with. Remember to adjust the pace based on your available time each day. Let's get started!

Phase 1: Fundamentals of Machine Learning (Days 1-20)

Day 1: Introduction to Machine Learning

Days 2-4: Python Basics for Machine Learning

Days 5-7: Mathematics for Machine Learning

Days 8-10: Exploratory Data Analysis (EDA)

  • Learn EDA techniques using Python libraries like Pandas and Matplotlib

  • Resource: Kaggle's "Python Data Science Handbook" by Jake VanderPlas (Chapter 4)

  • Resource: kaggle.com/learn/pandas

Days 11-12: Data Preprocessing

Days 13-15: Supervised Learning - Regression

Days 16-18: Supervised Learning - Classification

Day 19: Mini Project - Predicting Housing Prices

  • Apply regression techniques learned to predict housing prices using a public dataset

  • Resource: Kaggle's "House Prices: Advanced Regression Techniques" competition

Day 20: Mini Project - Titanic Survival Prediction

  • Build a classification model to predict survival on the Titanic using a public dataset

  • Resource: Kaggle's "Titanic: Machine Learning from Disaster" competition

Phase 2: Intermediate Machine Learning (Days 21-50)

Days 21-25: Unsupervised Learning

  • Explore clustering algorithms (K-means, hierarchical) and dimensionality reduction (PCA)

  • Resource: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron (Chapter 9)

  • Resource: kaggle.com/learn/unsupervised-learning

Days 26-28: Model Selection and Evaluation

  • Dive deeper into cross-validation, hyperparameter tuning, and model evaluation techniques

  • Resource: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron (Chapter 2 and 6)

  • Resource: kaggle.com/learn/model-validation

Days 29-33: Ensemble Methods

Days 34-37: Neural Networks and Deep Learning

Days 38-40: Convolutional Neural Networks (CNN)

  • Learn about CNN architecture, image classification, and transfer learning

  • Resource: "Deep Learning with Python" by François Chollet (Chapter 5)

  • Resource: kaggle.com/learn/computer-vision

Days 41-44: Natural Language Processing (NLP)

  • Explore techniques for text preprocessing, sentiment analysis, and language modeling

  • Resource: "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper (Chapter 1-3)

  • Resource: kaggle.com/learn/natural-language-processing

Day 45: Mini Project - Image Classification

  • Build a CNN model to classify images using a public dataset like CIFAR-10

  • Resource: Kaggle's "CIFAR-10 - Object Recognition in Images" competition

Day 46: Mini Project - Sentiment Analysis

  • Perform sentiment analysis on text data using NLP techniques

  • Resource: Kaggle's "Sentiment Analysis on Movie Reviews" competition

Days 47-50: Kaggle Competitions

  • Participate in Kaggle competitions to practice your skills and learn from others

  • Resource: Kaggle's Competitions page

  • Resource: kaggle.com/competitions

Phase 3: Advanced Topics and Real-world Applications (Days 51-100)

Days 51-55: Advanced Deep Learning

  • Explore advanced topics like recurrent neural networks (RNN), long short-term memory (LSTM), and generative adversarial networks (GANs)

  • Resource: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Chapter 10-20)

  • Resource: deeplearning.ai/deep-learning-specialization

Days 56-65: Cloud-based Machine Learning

  • Learn how to leverage cloud platforms like AWS, GCP, or Azure for scalable machine learning

  • Resource: AWS Machine Learning tutorials

  • Resource: GCP Machine Learning Resources

  • Resource: Azure Machine Learning tutorials

Days 66-70: Reinforcement Learning

  • Understand the basics of reinforcement learning and explore algorithms like Q-learning and Deep Q-Networks (DQN)

  • Resource: "Reinforcement Learning" by Richard S. Sutton and Andrew G. Barto

  • Resource: deeplearning.ai/deep-reinforcement-learning..

Days 71-80: Real-world Machine Learning Applications

  • Implement machine learning models in practical scenarios such as recommendation systems, fraud detection, or time series forecasting

  • Resource: Kaggle's "Real-world Machine Learning" course

  • Resource: kaggle.com/learn/real-world-machine-learning

Days 81-90: Deploying Machine Learning Models

Days 91-95: Ethics and Responsible AI

  • Understand the ethical implications of machine learning and AI, and explore fairness, accountability, and transparency

  • Resource: "Ethics of Artificial Intelligence and Robotics" by Vincent C. Müller

  • Resource: ai.google/education/responsible-ai-practices

Days 96-100: Final Project and Portfolio Development

  • Choose a machine learning project of your interest and work on it from start to finish

  • Document your project on GitHub and create a portfolio showcasing your skills and projects

While many of the resources I mentioned are freely available, some may require a paid subscription or have certain sections behind a paywall. However, there are still plenty of free resources and alternatives available to learn and practice machine learning.

Remember to constantly revise and practice concepts throughout the roadmap. Stay engaged with the machine learning community by participating in forums, attending webinars, and reading research papers. Good luck with your journey to landing a job in the machine learning field!

NOTE: I will keep updating the resource list when I find something interesting and feel free to update and expand your resource list as you discover new and interesting materials throughout your learning journey."