**What is Machine Learning? Why Machine Learning?[**

Motivation behind Machine Learning etc.

**Data Science for Internet of Things – The Big Picture**

We address the question: at what points can we add analytics to the data after it leaves the sensor and what are the implications of doing so at various stages.

This paper has three sections. The first section uses simulation to develop an intuitive understanding of the ideas behind Markov Chains; the second section looks at some of the mathematics used to represent the problem, leading to the standard eigenvector representation and the final section describes an idea to use Markov Chains together with a probability distribution model of social mobility to predict long term ‘social class’ proportions.

**How to easily do Topic Modeling with LSA, PSLA, LDA & lda2Vec**

This article is a comprehensive overview of Topic Modeling and it’s associated techniques.

If somebody asks you to guess how will be the weather tomorrow, you can take a guess. This guess is based on your knowledge of past weather. Predictive modelling gives this process a formal framework. It gives you tools to extract mathematical equations / rules from the past data to predict future results. Geisser [Predictive Inference: An Introduction] defines predictive modeling as “the process by which a model is created or chosen to try to best predict the probability of an outcome.”

ShinyProxy 1.1.1 is in essence a maintenance release, but there is one new feature that has been on the wish list of our users for a long time: the possibility of theming the landing page of ShinyProxy which displays the overview of the Shiny apps.

**Event Processing: Three Important Open Problems**

This article summarizes the three most important problems to be solved in event processing. The facts in this article are supported by a recent survey and an analysis conducted on the industry trends.

**10 More Free Must-Read Books for Machine Learning and Data Science**

1. Python Data Science Handbook

2. Neural Networks and Deep Learning

3. Think Bayes

4. Machine Learning & Big Data

5. Statistical Learning with Sparsity: The Lasso and Generalizations

6. Statistical inference for data science

7. Convex Optimization

8. Natural Language Processing with Python

9. Automate the Boring Stuff with Python

10. Social Media Mining: An Introduction

2. Neural Networks and Deep Learning

3. Think Bayes

4. Machine Learning & Big Data

5. Statistical Learning with Sparsity: The Lasso and Generalizations

6. Statistical inference for data science

7. Convex Optimization

8. Natural Language Processing with Python

9. Automate the Boring Stuff with Python

10. Social Media Mining: An Introduction

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