After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets. Unsupervised Machine Learning involves finding patterns in datasets. Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition. The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles). All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.

The Rubik's Cube is a famous 3-D puzzle toy. A regular Rubik's Cube has six faces, each of which has nine coloured stickers, and the puzzle is solved when each face has a united colour. If we count one quarter (90) turn as one move and two quarter turns (a "face" turn) as two moves, the best algorithms human-invented can solve any instance of the cube in 26 moves. My target is to let the computer learn how to solve the Rubik's Cube without feeding it any human knowledge like the symmetry of the cube. The most challenging part is the Rubik's Cube has 43,252,003,274,489,856,000 possible permutations.

If you are looking for a fast and quick introduction to python machine learning, then this course is for you. It is designed to give beginners a quick practical introduction to machine learning by doing hands-on labs using python and JupyterLab. I know some beginners just want to know what machine learning is without too much dry theory and wasting time on data cleaning. So, in this course, we will skip data cleaning. All datasets is highly simplified already cleaned, so that you can just jump to machine learning directly. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this course, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects.

It is a law of physics that everything that is not prohibited is mandatory. They are everywhere: in language, cooking, communication, image processing and, of course, computation. Mitigating and correcting them keeps society running. You can scratch a DVD yet still play it. QR codes can be blurred or torn yet are still readable. Images from space probes can travel hundreds of millions of miles yet still look crisp. Error correction is one of the most fundamental concepts in information technology.

I believe that one of the best ways to get the training you need for a job market in robotics is to attend tutorials at conferences like ICRA. Unlike workshops where you might listen to some work-in-progress, other workshop paper presentations and panel discussions, tutorials are exactly what they sound like. They aim to give you some hands-on learning sessions on technical tools/skills with specific learning objectives. As such, most tutorials would expect you to come prepared to actively participate and follow along. For instance, the "Tools for Robotic Reinforcement Learning" tutorial expects you to come knowing how to code in python and have basic knowledge of reinforcement learning because you'll be expected to use those skills/knowledge in the hands-on sessions. There are seven tutorials this year.

Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP? If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. Troubleshooting Python Machine Learning is the answer. Machine learning gives you powerful insights into data.

Welcome to experience the course "Digital Voice Cloning using Artificial Intelligence in 2022". Voice cloning technology on the Internet today is relatively accessible. This course focussed on "Digital Voice Cloning using Artificial Intelligence (AI) Tools" created by Digital Marketing Legend "Srinidhi Ranganathan" primarily deals with explaining a Montreal-based AI startup named "Lyrebird" which provides an online platform that, when trained on 30 or more recordings, can imitate a person's mimic speech. Lyrebird is an AI research division within Descript, currently and the team is building a new generation of tools for media editing and synthesis that make content creation more accessible and expressive. Sounding to be a wow factor, this new neural voice cloning technology from Lyrebird (that is discussed in the course) synthesises the voice of a human from audio samples fed to it.