Author: Iheanyi, Favour Chisom
Course: Zero-to-Mastery Machine Learning
Instructors: Daniel Bourke & Andrei Neagoie
This repository documents my journey through the Zero-to-Mastery Machine Learning Course by Daniel Bourke and Andrei Neagoie. It includes:
📖 Lecture Notes
🏋️ Exercises
🔬 Mini Projects
📝 Assignments
🤖 End-to-End Machine Learning Projects
📦 machine_learning
├── 📂 sample_pro/ # Main folder containing course materials
│ ├── 📂 matplotlib/ # Matplotlib-related notebooks and exercises
│ ├── 📂 numpy/ # NumPy-related notebooks and exercises
│ ├── 📂 pandas/ # Pandas-related notebooks and exercises
│ ├── 📂 projects/ # Machine learning mini-projects
│ ├── 📂 scikit-learn/ # Scikit-learn-related notebooks and exercises
│ ├── 📜 car-sales.csv # Dataset for data analysis
│ ├── 📜 heart-disease.csv # Dataset for ML model training
│ ├── 🖼️ heart_disease_analysis_plot.png # Visualization example
│ ├── 📜 introduction_to_numpy.ipynb # Notebook on NumPy basics
├── 📂 env/etc/ # Environment-related files
├── 📂 .ipynb_checkpoints/ # Auto-generated Jupyter Notebook checkpoints
├── 📜 requirements.txt # Dependencies for the project
└── 📜 README.md # Project documentation
Python (NumPy, Pandas, Matplotlib, Seaborn)
Scikit-Learn (Supervised & Unsupervised Learning)
TensorFlow & PyTorch (Deep Learning)
Jupyter Notebooks
Data Preprocessing & Exploratory Data Analysis
Supervised & Unsupervised Learning
Feature Engineering & Model Evaluation
Deep Learning with TensorFlow
Computer Vision & NLP
Deployment & Production-Ready Models
- Clone the repository:
git clone https://github.com/fachiny17/machine_learning.git cd machine_learning
- Install dependencies:
pip install -r requirements.txt
- Open Jupyter Notebook:
jupyter notebook
- Explore the notebooks and projects!
Implement more real-world ML projects
Optimize existing models for better performance
Deploy a machine learning model