# Tutorials, Overviews

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### Latest:

**Automated Feature Engineering for Time Series Data**- Nov 20, 2017.

We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.**Generative Adversarial Networks — Part II**- Nov 17, 2017.

Second part of this incredible overview of Generative Adversarial Networks, explaining the contributions of Deep Convolutional-GAN (DCGAN) paper.**Top 10 Videos on Deep Learning in Python**- Nov 17, 2017.

Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!**8 Ways to Improve Your Data Science Skills in 2 Years**- Nov 17, 2017.

Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.**Capsule Networks Are Shaking up AI – Here’s How to Use Them**- Nov 16, 2017.

If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.**PySpark SQL Cheat Sheet: Big Data in Python**- Nov 16, 2017.

PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing.**Basic Concepts of Feature Selection**- Nov 15, 2017.

Feature selection is a key part of data science but is it still relevant in the age of support vector machines (SVMs) and Deep Learning? Yes, absolutely. We explain why.**The 10 Statistical Techniques Data Scientists Need to Master**- Nov 15, 2017.

The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.**Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey**- Nov 14, 2017.

The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.**Extracting Tweets With R**- Nov 14, 2017.

This article will give you a great, brief overview for extracting Tweets using R.**Machine Learning Algorithms: Which One to Choose for Your Problem**- Nov 14, 2017.

This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.**The Qualitative Side of Quantitative Research**- Nov 9, 2017.

Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.**TensorFlow: What Parameters to Optimize?**- Nov 9, 2017.

Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.**7 Super Simple Steps From Idea To Successful Data Science Project**- Nov 8, 2017.

Ever had this great idea for a data science project or business? In the end you did not do it because you did not know how to make it a success? Today I am going to show you how to do it.**Tips for Getting Started with Text Mining in R and Python**- Nov 8, 2017.

This article opens up the world of text mining in a simple and intuitive way and provides great tips to get started with text mining.**Interpreting Machine Learning Models: An Overview**- Nov 7, 2017.

This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.**Real World Deep Learning: Neural Networks for Smart Crops**- Nov 7, 2017.

The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.**Blockchain Key Terms, Explained**- Nov 3, 2017.

Need a quick glance over some important definitions associated with the Blockchain? Then consider this article your Blockchain Definitions 101!**Want to know how Deep Learning works? Here’s a quick guide for everyone**- Nov 3, 2017.

Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.**Process Mining with R: Introduction**- Nov 2, 2017.

In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.**3 different types of machine learning**- Nov 1, 2017.

In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.**Conjoint Analysis: A Primer**- Nov 1, 2017.

Conjoint is another of those things everyone talks about but many are confused about…**Getting Started with Machine Learning in One Hour!**- Nov 1, 2017.

Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.

### October:

**7 Steps to Mastering Deep Learning with Keras****Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning****Updates & Upserts in Hadoop Ecosystem with Apache Kudu****Hello, World: Building an AI that understands the world through video****Density Based Spatial Clustering of Applications with Noise (DBSCAN)****Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation****No order left behind; no shopper left idle.****Top 10 Machine Learning with R Videos****TensorFlow: Building Feed-Forward Neural Networks Step-by-Step****Top 10 Machine Learning Algorithms for Beginners****5 Free Resources for Furthering Your Understanding of Deep Learning****7 Types of Artificial Neural Networks for Natural Language Processing****7 Techniques to Visualize Geospatial Data****Random Forests(r), Explained****How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool****Best practices of orchestrating Python and R code in ML projects****Edge Analytics – What, Why, When, Who, Where, How?****Learn Generalized Linear Models (GLM) using R****An opinionated Data Science Toolbox in R from Hadley Wickham, tidyverse****A Quick Guide to Fake News Detection on Social Media****The 5 Common Mistakes That Lead to Bad Data Visualization****How to Choose a Data Science Job****Deep Learning for Object Detection: A Comprehensive Review****A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)****Applied Data Science: Solving a Predictive Maintenance Business Problem****Using Machine Learning to Predict and Explain Employee Attrition****Neural Networks: Innumerable Architectures, One Fundamental Idea****XGBoost, a Top Machine Learning Method on Kaggle, Explained****Understanding Machine Learning Algorithms**