Welcome! It all began with the idea to challenge myself and accelerate my career in machine learning by embarking on a #100DaysOfCode journey, tailored specifically for AI, ML, DL, and Python engineering.
With the help of ChatGPT-4, I crafted a 100-day plan that would take me from beginner to advanced level, creating real-world projects each day.
ChatGPT helped me to design a spreadsheet spanning 100 days, meticulously organizing each day’s Challenge Title, Challenge Description, Anki Interview Q/A, and progress tracking across several areas, including GitHub, Twitter, networking, CodeWars, and blog publishing.
With the roadmap laid out, my 100 Days of ML odyssey have commenced, fueled by dedication, curiosity, and the desire to learn and grow. It will be updated each day, once I complete a project.
Please star if this is helpful for your journey!
Day 1: Development Environment Setup
- Set up your development environment: Python, Jupyter Notebook, Git, TensorFlow, and Keras
- Tweet about the setup process and create a blog post to document it
- Machine Learning Interview Anki Question: "What sets supervised learning apart from unsupervised learning?"
- Link: Project
Day 2: Basic Python Concepts
- Learn basic Python programming concepts (variables, data types, loops, conditionals, and functions)
- Practice by creating a simple Python script and uploading it to your GitHub repository
- Machine Learning Interview Anki Question: "What are the main types of machine learning?"
- Link: Project
Day 3: NumPy and Pandas Introduction
- Familiarize yourself with NumPy and Pandas
- Complete the introductory exercises in the Machine Learning Bootcamp course
- Machine Learning Interview Anki Question: "How do regression and classification differ?"
- Link:
Day 4: Data Visualization with Matplotlib and Seaborn
- Learn about data visualization with Matplotlib and Seaborn
- Create a blog post explaining the importance of data visualization and sharing examples of your own visualizations
- Machine Learning Interview Anki Question: "How does machine learning differ from deep learning?"
- Link:
Day 5: Simple Feedforward Neural Network
- Begin the "Getting Started with Deep Learning" course
- Implement a simple feedforward neural network for a toy dataset using TensorFlow and Keras
- Machine Learning Interview Anki Question: "What is a confusion matrix and its purpose?"
- Link: Project
Day 6: Loss Functions and Gradient Descent
- Study the concept of loss functions and gradient descent
- Implement gradient descent for linear regression and share the code on GitHub
- Machine Learning Interview Anki Question: "How are AI, ML, and Deep Learning related?"
- Link: Project
Day 7: Convolutional Neural Networks Basics
- Understand the basics of convolutional neural networks (CNNs)
- Implement a simple CNN for image classification using the MNIST dataset
- Machine Learning Interview Anki Question: "What is the trade-off between bias and variance?"
- Link:
Day 8: Basic Text Preprocessing
- Start the "Getting Started with Natural Language Processing" course
- Implement a basic text preprocessing pipeline (tokenization, stemming, and stopword removal)
- Machine Learning Interview Anki Question: "What distinguishes Lasso from Ridge?"
- Link: Project
Day 9: Word Embeddings
- Learn about word embeddings (Word2Vec, GloVe)
- Train your own word embeddings on a small text corpus and visualize the results
- Machine Learning Interview Anki Question: "Can you describe your favorite algorithm in under a minute? (Decision Trees...)"
- Link: Project
Day 10: Recurrent Neural Networks Basics
- Study recurrent neural networks (RNNs)
- Implement a simple RNN for text generation using TensorFlow and Keras
- Machine Learning Interview Anki Question: "How does KNN differ from k-means clustering?"
- Link: Project
Day 11: Data Preprocessing and Feature Engineering
- Learn about data preprocessing and feature engineering
- Apply these techniques to a real-world dataset and share the code on GitHub
- Machine Learning Interview Anki Question: "What is cross-validation and its methods?"
- Link: Project
Day 12: Decision Trees and Random Forests
- Explore decision trees and random forests
- Implement a decision tree classifier on a real-world dataset
- Machine Learning Interview Anki Question: "Explain the ROC curve."
- Link: Project
Day 13: Support Vector Machines
- Learn about support vector machines (SVMs)
- Implement an SVM classifier on a real-world dataset
- Machine Learning Interview Anki Question: "What differentiates probability and likelihood?"
- Link: Project
Day 14: Image Classification with Convolutional Neural Networks (CNN)
- Learn about CNNs and how to implement them using TensorFlow and Keras
- Project: Classify images from the CIFAR-10 dataset
- Machine Learning Interview Anki Question: "What's the difference between a generative and discriminative model?"
- Link: Project
Day 15: Natural Language Processing (NLP) Basics
- Learn about NLP, tokenization, and stemming
- Project: Perform text preprocessing on a given dataset
- Machine Learning Interview Anki Question: "What is decision tree pruning?"
- Link: Project
Day 16: Sentiment Analysis
- Learn about sentiment analysis and how to perform it using Python libraries
- Project: Analyze the sentiment of social media posts or news articles related to human rights issues, environmental justice, or other social causes
- Machine Learning Interview Anki Question: "How to select a classifier based on training set size?"
- Link: Project
Day 17: Text Classification with Naive Bayes
- Learn about the Naive Bayes algorithm and how to implement it for text classification
- Project: Create a classifier to categorize social media posts, news articles, or NGO reports into categories such as human rights, sustainability, or child advocacy
- Machine Learning Interview Anki Question: "What dimensionality reduction methods do you know and how do they compare?"
- Link: Project
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