You can find many free courses on the basics of machine learning through various platforms. One popular option is the "Machine Learning" course by Andrew Ng on Coursera, which offers foundational knowledge in machine learning concepts, algorithms, and applications. Another excellent resource is Google's "Machine Learning Crash Course," which provides a hands-on introduction with practical exercises. Additionally, platforms like edX, Udacity, and Khan Academy offer free introductory courses on machine learning and data science. You can also explore YouTube channels dedicated to AI and machine learning for tutorials and lectures.

Here are some popular platforms offering free courses on the basics of machine learning:

### 1. **Coursera – Machine Learning by Stanford University**

- **Instructor**: Andrew Ng

- **Duration**: ~11 weeks

- **Level**: Beginner to Intermediate

- **What you'll learn**:

- Supervised learning (linear regression, logistic regression)

- Neural networks, support vector machines

- Unsupervised learning (clustering, dimensionality reduction)

- Machine learning algorithms and practical applications

- **Link**: [Coursera Machine Learning](https://www.coursera.org/learn/machine-learning)

### 2. **Google AI – Machine Learning Crash Course**

- **Duration**: ~15 hours

- **Level**: Beginner

- **What you'll learn**:

- Core machine learning concepts

- Google’s TensorFlow library for building models

- Real-world examples and interactive visualizations

- **Link**: [Google ML Crash Course](https://developers.google.com/machine-learning/crash-course)

### 3. **edX – Principles of Machine Learning by Microsoft**

- **Duration**: Self-paced (~6 weeks)

- **Level**: Beginner

- **What you'll learn**:

- Introduction to machine learning models and algorithms

- Supervised and unsupervised learning

- How to evaluate model performance

- Applications in data mining, AI, and big data

- **Link**: [edX Machine Learning Course](https://www.edx.org/course/principles-of-machine-learning)

### 4. **Kaggle – Intro to Machine Learning**

- **Duration**: ~3 hours

- **Level**: Beginner

- **What you'll learn**:

- Basic machine learning concepts

- Decision trees and model validation

- Practical exercises with real datasets

- Hands-on experience with Python and scikit-learn

- **Link**: [Kaggle Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)

### 5. **Fast.ai – Practical Deep Learning for Coders**

- **Duration**: Self-paced

- **Level**: Beginner (with some coding experience)

- **What you'll learn**:

- Deep learning fundamentals and practical application

- How to build models from scratch using Python and PyTorch

- Hands-on projects with real-world datasets

- **Link**: [Fast.ai Practical Deep Learning](https://course.fast.ai/)

### 6. **Udemy – Introduction to Machine Learning for Data Science**

- **Duration**: ~3 hours

- **Level**: Beginner

- **What you'll learn**:

- Basic machine learning algorithms and concepts

- Data preprocessing and feature engineering

- Evaluation metrics and improving models

- **Link**: [Udemy Machine Learning](https://www.udemy.com/course/introduction-to-machine-learning-for-data-science/)

### 7. **OpenCourseWare – MIT's Intro to Machine Learning (6.036)**

- **Duration**: Semester-long (self-paced)

- **Level**: Intermediate

- **What you'll learn**:

- Core machine learning algorithms and techniques

- Mathematical foundations of ML

- Applications in real-world problems

- **Link**: [MIT Machine Learning Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-036-introduction-to-machine-learning-fall-2020/)

### 8. **YouTube – 3Blue1Brown’s Neural Networks Playlist**

- **Duration**: ~3-4 hours

- **Level**: Beginner

- **What you'll learn**:

- Fundamentals of neural networks and backpropagation

- Intuitive visualizations of how neural networks work

- **Link**: [3Blue1Brown Neural Networks](https://www.youtube.com/playlist?list=PLn8PRpmsu08pGQ0kpRkYrYHW9SMC3zdL7)

These courses cover various aspects of machine learning, from basic algorithms to neural networks, providing a solid foundation for beginners. You can choose one that suits your preferred learning style and pace!