This repository contains the exercises and code implementations from the ML Zoomcamp course by DataTalksClub. The course offers a comprehensive introduction to machine learning and covers various techniques, tools, and frameworks used to build and deploy machine learning models.
You can access the official course materials and additional resources from the following GitHub repository:
The ML Zoomcamp course provides hands-on experience with machine learning concepts and practices, making it ideal for anyone looking to develop their skills in the field of machine learning and AI. The course is divided into several key topics, each building upon the previous one, to ensure a strong understanding of both foundational and advanced machine learning techniques.
- Introduction to Machine Learning
Understanding the basics of machine learning, including the types of machine learning (mainly supervised and unsupervised learning), and an overview of the machine learning pipeline.
- Machine Learning for Regression
Exploring regression algorithms, which are used for predicting continuous values. It cover techniques like linear regression, regularization, and how to assess model performance.
- Machine Learning for Classification
Delving into classification problems, where the goal is to predict categorical outcomes. This section will explore popular classification algorithms such as logistic regression, k-nearest neighbors (KNN), and support vector machines (SVM).
- Evaluation Metrics for Classification
Learning about various evaluation metrics for classification models, such as accuracy, precision, recall, F1-score, and ROC-AUC, to understand how well our models perform and how to tune them for optimal results.
- Deploying Machine Learning Models
Understanding how to take a trained model and deploy it for real-world use. This section will guide you through deploying models in production environments, making predictions in real-time, and integrating models into applications.
- Decision Trees and Ensemble Learning
An introduction to decision trees, which are a powerful and interpretable machine learning model. This section also covers ensemble learning methods like random forests and gradient boosting that combine multiple models to improve accuracy and robustness.
- Neural Networks and Deep Learning
Diving into neural networks, which form the foundation of deep learning. This section covers the structure of neural networks, backpropagation, activation functions, and techniques for training deep neural networks.
- Serverless Deep Learning
Exploring serverless computing in the context of deep learning. This section teaches how to deploy deep learning models using serverless platforms, eliminating the need for managing infrastructure.
- Kubernetes and TensorFlow Serving
An introduction to containerization using Kubernetes, and how to deploy deep learning models at scale with TensorFlow Serving, allowing for efficient, scalable serving of machine learning models.
This repository contains code for all the exercises and projects covered throughout the course. Each exercise is organized into its own directory, with the following structure:
/Week_Number
/Week_Number.ipynb
/Week_Number
/Week_Number.ipynb
/Additional_File(s)
...
requirements.txt
README.md
To run the code in this repository, make sure you have Python 3 installed on your machine. The repository includes a requirements.txt file that lists the additional Python libraries required to run the notebooks seamlessly. You can install these dependencies using pip:
pip install -r requirements.txt
Additionally, some exercises utilize Docker for containerized environments. Make sure you have Docker installed and properly configured if you're working on those specific exercises.
For each exercise, you can run the code by following the steps below:
git clone https://github.com/AnkS4/mlzoomcamp
cd mlzoomcamp
cd 1/
Run the Python script:
python script_name.py
Run the Jupyter notebook:
jupyter notebook