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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", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.
Machine learning is 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?
In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.
It’s important to know both the advantages and disadvantages of each algorithm we look at.
Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.
We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.
Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.
The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.
One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.
We’ll do a comparison with deep learning so you understand the pros and cons of each approach.
We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.
We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.
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.
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: supervised_class
Make sure you always "git pull" so you have the latest version!
TIPS (for getting through the course):
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|Section 1: Introduction and Review|
Introduction and OutlinePreview
Review of Important ConceptsPreview
Where to get the Code and Data
|Section 2: K-Nearest Neighbor|
K-Nearest Neighbor Concepts
KNN in Code with MNIST
When KNN Can FailPreview
KNN for the XOR Problem
KNN for the Donut Problem
|Section 3: Naive Bayes and Bayes Classifiers|
Naive Bayes Handwritten Example
Naive Bayes in Code with MNIST
Bayes Classifier in Code with MNIST
Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
Generative vs Discriminative Models
|Section 4: Decision Trees|
Decision Tree Basics
Maximizing Information Gain
Choosing the Best Split
Decision Tree in Code
|Section 5: Perceptrons|
Perceptron in Code
Perceptron for MNIST and XOR
Perceptron Loss Function
|Section 6: Practical Machine Learning|
Hyperparameters and Cross-Validation
Feature Extraction and Feature Selection
Comparison to Deep Learning
Regression with Sci-Kit Learn is Easy
|Section 7: Building a Machine Learning Web Service|
Building a Machine Learning Web Service Concepts
Building a Machine Learning Web Service Code
|Section 8: Conclusion|
What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)
|Section 9: Appendix|
How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
Where to get Udemy coupons and FREE deep learning material
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.
I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.