In recent years, we've seen a resurgence in **AI**, or **artificial intelligence**, and **machine learning**.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

**Google** famously announced that they are now "machine learning first", and companies like **NVIDIA** and **Amazon** have followed suit, and this is what's going to drive innovation in the coming years.

Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

This course is all about**ensemble methods**.

We've already learned some classic machine learning models like**k-nearest neighbor** and **decision tree**. We've studied their limitations and drawbacks.

But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?

In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.

In particular, we will study the**Random Forest** and **AdaBoost** algorithms in detail.

To motivate our discussion, we will learn about an important topic in statistical learning, the**bias-variance trade-off**. We will then study the **bootstrap** technique and **bagging** as methods for reducing both bias and variance simultaneously.

We'll do plenty of**experiments** and use these algorithms on **real datasets** so you can see first-hand how powerful they are.

Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on**"how to build and understand"**, not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about **"seeing for yourself" via experimentation**. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Suggested Prerequisites:

Tips for success:

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

This course is all about

We've already learned some classic machine learning models like

But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?

In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.

In particular, we will study the

To motivate our discussion, we will learn about an important topic in statistical learning, the

We'll do plenty of

Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on

Suggested Prerequisites:

- calculus
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- Linear Regression, Logistic Regression
- K-Nearest Neighbor, Decision Trees

Tips for success:

- Use the video speed changer! Personally, I like to watch at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don't, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
- Write code yourself, this is an applied course! Don't be a "couch potato".

- Outline and Motivation (05:40) (FREE preview available)
- Where to get the Code and Data (09:21)
- All Data is the Same (03:15)
- Plug-and-Play (02:11)

- Bias-Variance Key Terms (06:37)
- Bias-Variance Trade-Off (03:09)
- Bias-Variance Decomposition (03:32)
- Polynomial Regression Demo (18:07)
- K-Nearest Neighbor and Decision Tree Demo (06:32)
- Cross-Validation as a Method for Optimizing Model Complexity (04:26)
- Suggestion Box (03:03)

- Bootstrap Estimation (09:55)
- Bootstrap Demo (05:20)
- Bagging (02:37)
- Bagging Regression Trees (07:19)
- Bagging Classification Trees (08:39)
- Stacking (03:54)

- Random Forest Algorithm (08:54)
- Random Forest Regressor (07:05)
- Random Forest Classifier (04:56)
- Random Forest vs Bagging Trees (03:47)
- Implementing a 'Not as Random' Forest (04:12)
- Connection to Deep Learning: Dropout (02:38)

- AdaBoost Algorithm (07:09)
- Additive Modeling (01:50)
- AdaBoost Loss Function: Exponential Loss (07:15)
- AdaBoost Implementation (08:26)
- Comparison to Stacking (03:29)
- Connection to Deep Learning (03:48)
- Summary and What's Next (04:55)

- Confidence Intervals (10:17)

- Windows-Focused Environment Setup 2018 (20:20)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)

- How to Code Yourself (part 1) (15:54)
- How to Code Yourself (part 2) (09:23)
- Proof that using Jupyter Notebook is the same as not using it (12:29)
- Python 2 vs Python 3 (04:38)

- How to Succeed in this Course (Long Version) (10:24)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
- What order should I take your courses in? (part 1) (11:18)
- What order should I take your courses in? (part 2) (16:07)

- What is the Appendix? (02:48)
- Where to get discount coupons and FREE deep learning material (05:31)