Pye AI News

Learn Data Science in 2021: Books, Blogs, YouTube channels & more

3 weeks ago2 weeks ago Oleksii Kharkovyna

As a curious person and practitioner in the field of AI, ML, and Data Science, I never stop my research, and I see myriads of blogs everyday. So, I decided to gather the most insightful and valuable stuff (as I see it) in one place to help others in their growth. You will not find mainstream stuff here or something everyone already knows. Here are my top picks of fresh material and content you can read, watch, or listen to. So let’s start our adventure!

Time Series Analysis Types for Machine Learning Applications (With Examples)

3 weeks ago2 weeks ago Kurt F.

We continue to focus on Time Series Analysis. We will estimate the fertility rate for 2021 in Germany by all methods. In this article, we will simply give some examples for a better understanding of time series.

A Bayesian take on Model Regularization

1 month ago1 month ago Ryan Sander

In this article, we explore how we can, and do, regularize and control the complexity of the models we learn through Bayesian prior beliefs. I’m currently reading “How We Learn” by Stanislas Dehaene. First off, I cannot recommend this book enough to anyone interested in learning, teaching, or AI.

Markov Chain explained

1 month ago1 month ago Vatsal

In this article, I will explain and provide the python implementations of Markov chain. This article will not be a deep dive into the mathematics behind Markov chains, instead, it will prioritize the conceptual understanding of how it works and how to implement it with python. I left resources I’ve used and other materials at the bottom of this article which goes into a deep dive in the mathematics behind Markov chains.

Modelling Risk: the absolute and the relative

2 months ago2 months ago Dr Andreas Ochs

What is the risk of a new creditor to default on their loan? what is the “risk” of watching a certain movie on Netflix given a certain viewing history? What is my risk of dying given a certain diagnosis and how is this affected by a certain treatment? Risks are all around us, and quantifying these risks is becoming increasingly popular. Providing the right kind of analysis to these key questions is crucial to making the right decisions.

15 Free Data Science, Machine Learning & Statistics eBooks for 2021

2 months ago2 months ago Matthew Mayo

We present a curated list of 15 free eBooks compiled in a single location to close out the year. Among other articles highlighting such materials, I have written a series of posts since the pandemic erupted, in the case that more people spending more time at home may result in more time for reading.

Simulating the FIFA World Cup 2022

2 months ago2 months ago Abhinav Raghunathan

Who does the data choose to win the largest international football tournament yet? The grandest and most exciting of all football tournaments is still a ways off (2022), but in times like these I find solace in the fact that there are better things (like the next World Cup) that are rapidly approaching with every day that passes. The question on everyone’s mind is always: who wins? My mission is to see what the data says.

The danger of overfitting a Model

2 months ago2 months ago Andrew Udell

An Explanation for Splitting Data into Training and Testing Sets adn teh danger of overfitting.

A Quick Introduction to Time Series Analysis

3 months ago2 months ago Robby Sneiderman

In my first article on Time Series, I hope to introduce the basic ideas and definitions required to understand basic Time Series analysis. We will start with the essential and key mathematical definitions, which are required to implement more advanced models. The information will be introduced in a similar manner as it was in a McGill graduate course on the subject, and following the style of the textbook by Brockwell and Davis.

Top 5 tech Startup Trends in 2020

4 months ago4 months ago Alina

According to Statista, the global startup economy generates nearly $3 trillion. Startups have always been the driving force behind the world market, bringing new ideas, and transforming familiar business systems. Unfortunately, the economic crisis associated with Covid-19 has not spared this area.

Ockham’s Spatula

4 months ago4 months ago Charlie Kufs

The science and the art of model deployment. Model building is like climbing a mountain. It’s what you spend so much time planning for. It’s what everybody wants to talk about. It’s what gives you that euphoric feeling of accomplishment when you’re finished.

An introduction to possibility theory

4 months ago4 months ago Mahmoud Albardan

Possibility theory was introduced by Zadeh [1] and further developed by Dubois and Prade [2] with the motivation to offer a well-defined and formal mathematical representation for linguistic statements that permits handling imprecise or vague information.

The evolution of Data Science … as I remember it

4 months ago4 months ago Charlie Kufs

History isn’t always clear-cut. It’s written by anyone with the will to write it down and the forum to distribute it. It’s valuable to understand different perspectives and the contexts that created them. The evolution of the term Data Science is a good example.

Data Science Life Cycle: A disciplined approach to Data Science

5 months ago5 months ago Kevin Allen

Considering the Data Science Life Cycle as a life cycle enables a natural consideration of crucial overarching factors such as reproducibility, documentation and meta data, ethics, and archiving of research artefacts such as data and code.

The importance of Hypothesis Testing

5 months ago5 months ago saurav singla

Hypothesis tests are significant for evaluating answers to questions concerning samples of data. What is the value of hypothesis testing to AI models.

Data analysis without programming

5 months ago5 months ago Victor Silva

Data analytics do not always require complicated programming. Applications can be achieved sometimes in a simpler way.

The Data Scientist’s Toolbox (part 1)

5 months ago5 months ago Benedict Neo

Data is the ingredient for data science processes, and by understand what data is can help you to be more efficient and appreciate what data science is all about.

An overview of the global market for chatbot solutions in 2020

5 months ago5 months ago Patrick Meyer

Coming from my active watch on the conversational assistant market, I give you some statistics and information on the almost 550 solutions I studied. As a conclusion, I will give you my view about the future.

The last Machine & Deep-Learning Compendium you’ll ever need

5 months ago5 months ago Ori Cohen

In the last 3 years, I have been curating everything related, directly or indirectly, to machine-learning (ML), deep-learning (DL), Statistics, Probability, NLP, NLU, deep-vision, etc. I started curating a compendium because I wanted to expand the scope of my knowledge. I believe that every researcher and data scientist (DS) should strive to learn more on a daily basis, not by hitting task-related walls and solving them, but as a lifelong learning practice.

Principal Components Analysis explained

6 months ago6 months ago John Clements

In real world data sets, many of our variables are unimportant or correlated with each other. If we are doing a supervised machine learning task, leaving in variables unrelated to our target variable lowers the signal to noise ratio in our training data, making it harder to construct a good model.

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