Probabilistic modelling, which falls under the Bayesian paradigm, is gaining popularity world-wide. Its powerful capabilities, such as giving a reliable estimation of its own uncertainty, makes Gaussian process regression a must-have skill for any data scientist. Gaussian process regression is especially powerful when applied in the fields of data science, financial analysis, engineering and geostatistics. This course covers the fundamental mathematical concepts needed by the modern data scientist to confidently apply Gaussian process regression. The course also covers the implementation of Gaussian process regression in Python.

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.

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.

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.

End to End machine learning course

An online course with an AI tutor achieves a significantly higher completion rate than traditional online courses thanks to a personalized learning experience. Does Artificial Intelligence enable quality education for everyone? Artificial Intelligence can revolutionize education: Instead of mass teaching, AI tutors could provide personalized, active and hands-on learning experiences. This can be a useful complement to the crowded classroom, the dry lecture, and especially in combination with online courses, which are used by millions of people around the world to learn every day. The latter scenario in particular plays a central role in the plans of learning platform startup Korbit, as only a small portion of humanity has access to quality education.

This is a Practice test to test you skills in Machine learning skills like Confusion Matrix, Regression etc. The test primarily focuses on regression and Confusion Matrix Grill. This test wil help you to prepare your self throughly for confusion matrix for sure. Every question has been provided with an answer and its explaination where ever needed. Confusion matrix is one for the most confusing topics and hence more weightage has been given to it.

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. Value estimation--one of the most common types of machine learning algorithms--can automatically estimate values by looking at related information.

In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.

This course is designed to teach the student the concepts of supervised and unsupervised machine learning by experimenting on the toy datasets that are installed in Python's machine learning library, sklearn. The student will learn the basics of coding in the Python programming language and then will learn the basics of machine learning by studying a very small dataset and the code that has been used to make predictions on it. When the student has learned the basics of programming in Python and making predictions on a very small movie recommendation dataset, he will go on to study the eight toy datasets that are installed in sklearn, which is Python's machine learning library. The student will study the code of the above dataset and will learn the basics of supvervised machine learning, which involves making predictions on labeled datasets to answer either regression or classification problems. The students will also go over the code on an unsupervised learning technique, clustering.