The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by having Q & A interviews with experts such as Mike Olsen, Hal Varian, Tom Davenport, and data scientists at LinkedIn, Facebook, Yelp, and more. The Data Analytics Handbook was not only instrumental in giving us the understanding we needed to feel confident in what we were creating; but was downloaded over 25,000 times, gave us dozens of contacts, and an immediate group of early adopters. Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others.

Please note that we can get an idea of a possible skew in the data by comparing the mean to the median, i.e. the 50% figure.

ML builds heavily on statistics. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data. If the training set is not random, we run the risk of the machine learning patterns that aren't actually there. And if the training set is too small (see law of large numbers), we won't learn enough and may even reach inaccurate conclusions. For example, attempting to predict company-wide satisfaction patterns based on data from upper management alone would likely be error-prone.

Data Scientists enjoy one of the top-paying jobs, with an average salary of 120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers.

Summary: I learn best with toy code that I can play with. This tutorial teaches gradient descent via a very simple toy example, a short python implementation. Followup Post: I intend to write a followup post to this one adding popular features leveraged by state-of-the-art approaches (likely Dropout, DropConnect, and Momentum). Feel free to follow if you'd be interested in reading more and thanks for all the feedback! In Part 1, I laid out the basis for backpropagation in a simple neural network. Backpropagation allowed us to measure how each weight in the network contributed to the overall error. This ultimately allowed us to change these weights using a different algorithm, Gradient Descent.

Collaborative filtering is the process of filtering for information using techniques involving collaboration among multiple agents. Applications of collaborative filtering typically involve very large data sets. This article covers some good tutorials regarding collaborative filtering we came across in Python, Java and R. Crab engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. The tutorial is from official documentation of Crab. This article presents an implementation of the collaborative filtering algorithm, that filters information for a user based on a collection of user profiles.

This class is self paced. You can begin whenever you like and then follow your own pace. It's a good idea to set goals for yourself to make sure you stick with the course. Take a look at the "Class Summary," "What Should I Know," and "What Will I Learn" sections above. If you want to know more, just enroll in the course and start exploring.

Telstra Network Disruptions challenged Kagglers to predict the severity of service disruptions on their network. Using a dataset of features from their service logs, participants were tasked with predicting if a disruption was a momentary glitch or a total interruption of connectivity. Mario Filho, a self-taught data scientist, took first place in his first "solo win". In this blog, he shares a high-level view of his approach. My background in machine learning is completely "self-taught". It all began in 2012 when I decided to learn Calculus on my own through the videos from a MIT class.