Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible. This team has decades of practical experience in working with Java and with billions of rows of data. 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.

About this course: The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

A staggering amount of data is created everyday; analyzing and organizing this enormous amount of data can be quite a complex task. Haskell is a powerful and well-designed functional programming language that is designed to work with complex data. It is trending in the field of data science as it provides a powerful platform for robust data science practices. This course will introduce the basic concepts of Haskell and move on to discuss how Haskell can be used to solve the issues by using the real-world data. The course will guide you through the installation procedure, after you have all the tools that you require in place, you will explore the basic concepts of Haskell including the functions, and the data structures.

About this course: This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

This course helps to understand what AI is, how it works, and how to use it to build smart apps. You can learn how to build simple machine learning models and implement conversational bots. To learn about machine learning, enter this course to get both theoretical and practical knowledge. You will understand various concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor.

The field of computer science is exceptionally vast and ever-expanding. It will take a lifetime to just fathom its depth, forget mastering all the diversified fields. However, it's'programming' which is ubiquitous in the various branches of computer science. Programming offers a plethora of opportunities to kick-start your professional career. Now if you dabble in the art of coding (the other term for'programming'), yet the multitude of options confuses you, pore over the following article to find your niche in computer science.

Welcome to this course: The Complete Natural Language Processing (NLP) Course. Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Natural Language Processing (NLP) is used in many applications to provide capabilities that were previously not possible. It involves analyzing text to obtain intent and meaning, which can then be used to support an application. This comprehensive course will get you up-and-running with advanced tasks using Natural Language Processing Techniques with Python.

It goes without saying that Natural Language Processing, or NLP, is one of the most important and demanded technologies of the present day. You can find it everywhere as people communicate almost everything in language: it is present in web searches, advertisement, emails, customer service, language translation, summaries, etc. On my first acquaintance with NLP, I remember watching an online lecture where a lady was saying: "NLP is easy. Just install the NLTK library and with a minimum experience in Python you can start experimenting". Time flies, as our QA engineers like saying in their test logs.

In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.

Make your first mobile app and game here. Learn how to code and make games in the popular Unreal Engine 4. Learn by building 6 actual games. Make next-level apps that use machine learning with Java, Android, TensorFlow Estimator, PyCharm, and MNIST. By taking this course you will make 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market prediction project, and text-response project.