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Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. In Reinforcement learning, the machine learns from its mistakes. Reinforcement learning helps determine if an algorithm is producing a correct right answer or a reward indicating it was a good decision.

Python is considered the easiest high-level, general-purpose programming language to learn, allowing you to build portable, cross-platform applications. This, along with its dynamic garbage collection and simple, concise code, makes it ideal for applications related to artificial intelligence. But how do you go from writing a simple "Hello World" app to using Python for more sophisticated projects? The following guide will introduce nine resources that can help you master Python. InventWithPython.com is a website created and maintained by Al Sweigart, a professional software developer who has dedicated much of his time to teaching people how to code. Invent With Python provides you with a host of resources (mostly in an eBook form) to help you start coding with Python.

This repository is a tutorial to help learn NLP using Pytorch. This tutorial shows you how to implement state-of-the-art models with less than 100 lines of code. This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. This repository focuses on NLP models that are popular with researchers and practitioners working on problems involving text and language. This course includes lecture and seminar materials about NLP for each week.

Google AI announced the launch of the Google Research YouTube channel today. The channel is set to focus on a wide range of subjects like AI/ML, robotics, theory and algorithms, quantum computing, health and bioscience. The show will introduce viewers to a Google Researcher who will explain about their innovations and the implications of the newly emerging technologies in our daily lives. In its first rendition, Drew Calcagno spoke with Google Researchers Sharan Narang and Aakanksha Chowdhery, who theorised and introduced the language models to robotics and coded the Pathways Language Model (PaLM). This series focuses on the conversion of research publications by Google researchers into byte-sized content for viewers.

Mathematics is the core of all machine learning algorithms. And while it isn't a prerequisite to have formal math education in order to become a data scientist, you need to understand the principles of the subject well enough to successfully build models that add value. In an article I wrote previously, I explained the three branches of mathematics that were essential to gain a deeper understanding of ML algorithms -- statistics, calculus, and linear algebra. This article will solely focus on linear algebra, as it forms the backbone of machine learning model implementation. Linear algebra concepts like vectorization allow for faster computation speeds, and are implemented in libraries like Pandas, Scipy, and Scikit-Learn.

Do you have a question, "How to Learn Data Analytics for Free?"… If yes, this article is for you. In this article, you will get a step by step guidance along with the free resources to learn data analytics. So without any further ado, let's get started- Before I answer this question, let's have a look at mandatory skills for Data Analysts. So, these are must-have skills for a Data Analysts.

Do you have a question, "How to Learn Data Science From Scratch for Free?"… If yes, this article is for you. In this article, you will get a step by step free resources to learn data science from scratch. So without any further ado, let's get started- Before I answer this question, let's have a look at mandatory skills for Data Science. So, these are must-have skills for a data scientist.

Not a week goes by without hearing about new applications of computer vision. If you take a look at the job market for machine learning, you'll notice that there are so many companies using computer vision to do all sorts of cool things. This is thanks to deep learning! I've seen mobile apps that use computer vision to tell you how many calories you have in your food from a picture of your plate. I've seen products that use computer vision to detect ships docked in the port.

Artificial Intelligence (or AI) is already playing a role and it is changing the markets in many ways. In this blog post, the author shares his views on the repercussions of this transformation. What you'll learn: Covered Call, Protective Put, Iron Condor, Bull Call, Bear Put