Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of principal component analysis is dimensionality reduction. Besides using PCA as a data preparation technique, we can also use it to help visualize data. A picture is worth a thousand words. With the data visualized, it is easier for us to get some insights and decide on the next step in our machine learning models.

After the article in Nature about the open-source of AlphaFold v2.0 on GitHub by DeepMind, many in the scientific and research community have wanted to try out DeepMind's AlphaFold implementation firsthand. With compute resources through Amazon Elastic Compute Cloud (Amazon EC2) with Nvidia GPU, you can quickly get AlphaFold running and try it out yourself. In this post, I provide you with step-by-step instructions on how to install AlphaFold on an EC2 instance with Nvidia GPU. The process starts with a Deep Learning Amazon Machine Image (DLAMI). After installation, we run predictions using the AlphaFold model with CASP14 samples on the instance.

In this course, we look at core Linear Algebra concepts and how it can be used in solving real world problems. We shall go through core Linear Algebra topics like Matrices, Vectors and Vector Spaces. If you are interested in learning the mathematical concepts in linear algebra, but also want to apply those concepts to datascience, statistics, finance, engineering, etc.then this course is for you! We shall explain detaily all Maths Concepts and also implement them programmaticaly in Python. We lay much emphasis on feedback.

This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification. We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward.

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop. Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks.

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 cipher decryption algorithm.

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

Luca Arrotta is Ph.D. student at the Department of Computer Science of the University of Milan. His interests are Machine Learning, Data Analysis, IoT, Mobile Programming, and Indoor Positioning. His research currently focuses on Pervasive Computing, Context-awareness, Explainable AI, and Human Activity Recognition in smart environments.

Imperial College London (ICL) is a world-class university with a mission to benefit society through excellence in science, engineering, medicine and business. It leads on Work Package 5 (WP5) of the Innovative Medicines Initiative (IMI) DRAGON project. Within the DRAGON project, ICL's contribution is led by Dr. Guang Yang and Dr. Simon Walsh with the aim to: 1. promote the performance of general clinical related tasks (such as classification, detection, and segmentation); 2. improve the performance of clinical diagnosis, and identify novel acquisition methods, such as fast CT to facilitate the delivery of standardised quantitative image biomarkers; and 3. undertake a comparative assessment of the effectiveness of the different approaches for enhancing the reproducibility of extracted quantitative image biomarkers. This will help with the validation of privacy preserved and self adaptive imaging as well as will lead to a deep generative model to support multicentre, cross-border clinical data sharing.

Be it Andrew Ng's ML/DL course on YouTube or any Data Science Bootcamp, you will need a certain degree of mathematical and statistical knowledge to not only understand but make a long-lasting, robust career as a data professional. This is a short and precise guide for all autodidact and beginners in the field of Data Science and Machine Learning. A common question that pops out from all my training programs, LinkedIn courses, videos on YT, or newsletters is that when they start learning DS/ML, after a certain point, they feel lost in mathematics or statistics and sometimes programming. And I have always recommended learning or refreshing some mathematical concepts that underpin ML as it helps you build intuition which keeps you curious throughout your learning journey.