# Starting Your AI Career Has Never Been Easier

#BreakIntoAI with the new Machine Learning Specialization, an updated foundational program for beginners created by Andrew Ng.

Presenting the new

## Machine Learning Specialization

### About the original course

2012

Year launched

Rated 4.9 out of 5 by 170K learners

4.8 Million

Learners enrolled

### About the instructor

A pioneer in the AI industry, Andrew Ng co-founded Google Brain and Coursera, led AI at Baidu, and has reached and impacted millions of learners with his machine learning courses.

## How the Machine Learning Specialization can help you

Newly rebuilt and expanded into 3 courses, the updated Specialization teaches foundational AI concepts through an intuitive visual approach, before introducing the code needed to implement the algorithms and the underlying math.

### I’m a complete beginner

- Doesn’t require prior math knowledge or a rigorous coding background
- Takes the core curriculum — vetted by millions of learners over the years — and makes it more approachable
- Each lesson begins with a visual representation of machine learning concepts, followed by the code, followed by optional videos explaining the underlying math

### I enrolled in but didn’t complete the original Machine Learning course

- Doesn’t require prior math knowledge or a rigorous coding background
- Balances intuition, code practice, and mathematical theory to create a simple and effective learning experience
- Includes new ungraded code notebooks with code samples and interactive graphs to help you complete graded assignments

### I’ve already completed the original Machine Learning course

- Great way to refresh foundational ML concepts
- Assignments and lectures have been rebuilt to use Python rather than Octave
- The section on applying machine learning has been updated significantly based on emerging best practices from the last decade
- Not for you? Take the next step with the Deep Learning Specialization!

## What Learners Are Saying

- 3 Courses
- 2.5 months (5 hours/week)
- Introductory

## Skills you will gain

- Linear Regression
- Logistic Regression
- Neural Networks
- Decision Trees
- Recommender Systems
- Supervised Learning
- Logistic Regression for Classification
- Gradient Descent
- Regularization to Avoid Overfitting
- Tensorflow
- Tree Ensembles
- XGBoost
- Advice for Model Development
- Unsupervised Learning
- Anomaly Detection
- Collaborative Filtering
- Reinforcement Learning

## Syllabus

## Course Slides

You can download the annotated version of the course slides below.

*Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes.