Instructional Material



[P]I wrote a tutorial about Inverse Reinforcement Learning and three basic algorithms. More to follow. • r/MachineLearning

@machinelearnbot

This idea is really interesting. Sadly I don't have nearly enough linear algebra experience to understand the details though. Would IRL still be feasible if the state was not explicit? It seems like this technique depends on prior knowledge of the state machine, but from what I understand about deep reinforcement learning, the state may be very complex, and the value function could actually be a deep neural network.


A Gentle Introduction to Matrix Factorization for Machine Learning - Machine Learning Mastery

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Many complex matrix operations cannot be solved efficiently or with stability using the limited precision of computers. A Gentle Introduction to Matrix Decompositions for Machine Learning Photo by mickey, some rights reserved. Take my free 7-day email crash course now (with sample code). Click to...


Unsupervised Deep Learning in Python Udemy

@machinelearnbot

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these...


The Value of Exploratory Data Analysis

@machinelearnbot

From the outside, data science is often thought to consist wholly of advanced statistical and machine learning techniques. However, there is another key component to any data science endeavor that is often undervalued or forgotten: exploratory data analysis (EDA). At a high level, EDA is the practic...


Ensemble Machine Learning in Python: Random Forest, AdaBoost

@machinelearnbot

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 i...



Cluster Analysis and Unsupervised Machine Learning in Python

@machinelearnbot

Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a r...


Open Machine Learning Course. Topic 2. Visual data analysis with Python

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In the field of Machine Learning, data visualization is not just making fancy graphics for reports; it is used extensively in day-to-day work for all phases of a project. To start with, visual exploration of data is the first thing one tends to do when dealing with a new task. We do preliminary checks and analysis using graphics and tables to summarize the data and leave out the less important details. It is much more convenient for us, humans, to grasp the main points this way than by reading many lines of raw data. It is amazing how much insight can be gained from seemingly simple charts created with available visualization tools. Next, when we analyze the performance of a model or report results, we also often use charts and images. Sometimes, for interpreting a complex model, we need to project high-dimensional spaces onto more visually intelligible 2D or 3D figures. All in all, visualization is a relatively fast way to learn something new about your data. Thus, it is vital to learn its most useful techniques and make them part of your everyday ML toolbox. In this article, we are going to get hands-on experience with visual exploration of data using popular libraries such as pandas, matplotlib and seaborn. The following material is better viewed as a Jupyter notebook and can be reproduced locally with Jupyter if you clone the course repository. Before we get to the data, let's initialize our environment: In the first article, we looked at the data on customer churn for a telecom operator. We will load again that dataset into a DataFrame: To get acquainted with our data, let's look at the first 5 entries using head():


Convolutional Neural Networks For All Part II – Machine Learning World – Medium

#artificialintelligence

If you're not a Deep Learning expert, chances are that the Coursera Convolutional Neural Networks course kicked your behind. So much information, so many complex theories covered in such a short time! Countless times pausing the lectures, rereading additional material and discussing topics later led us, a group of official mentors, to decide a learner study guide is worth the effort. Part I reviews the broad concepts covered in this course. Part II summarizes every single lecture for you. Part III will offer a deeplearning.ai dictionary to help you sort through the jungle of acronyms, technical terms and occasional jokes from grandmaster Ng once we've finished course 5. Let's dive deeper into the bewilderment of interesting information by recapping every lecture.