Master Apache Spark - Hands On! - Learn how to slice and dice data using the next generation big data platform - Apache Spark! Some basic Java programming experience is required. A crash course on Java 8 lambdas is included You will need a personal computer with an internet connection. The software needed for this course is completely freely and I'll walk you through the steps on how to get it installed on your computer Some basic Java programming experience is required. You will need a personal computer with an internet connection.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.

Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inference. Active inference has seen growing applications in academic research, especially in fields that seek to model human or animal behavior. While in recent years, some of the code arising from the active inference literature has been written in open source languages like Python and Julia, to-date, the most popular software for simulating active inference agents is the DEM toolbox of SPM, a MATLAB library originally developed for the statistical analysis and modelling of neuroimaging data. Increasing interest in active inference, manifested both in terms of sheer number as well as diversifying applications across scientific disciplines, has thus created a need for generic, widely-available, and user-friendly code for simulating active inference in open-source scientific computing languages like Python. The Python package we present here, pymdp (see https://github.com/infer-actively/pymdp), represents a significant step in this direction: namely, we provide the first open-source package for simulating active inference with partially-observable Markov Decision Processes or POMDPs. We review the package's structure and explain its advantages like modular design and customizability, while providing in-text code blocks along the way to demonstrate how it can be used to build and run active inference processes with ease. We developed pymdp to increase the accessibility and exposure of the active inference framework to researchers, engineers, and developers with diverse disciplinary backgrounds. In the spirit of open-source software, we also hope that it spurs new innovation, development, and collaboration in the growing active inference community.

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. Most topics include hands-on Python code examples you can use for reference and for practice. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

DBT data build tool helps data teams work like software engineers, transform data and control the flow to ship trusted data, faster. It means that we first load the data as is to the target and then use SQL (DBT data build tool) to transform it. DBT data build tool will materialize your SQL selects into table views and manage the flow of executing the SQL. ETL developers, DBA, BI developers, decision-makers that consider DBT, SQL programmers, data analysts, data engineers.

Learn The Theory of Deep Learning in the most comprehensive and up-to-date course on the topic created by The Click Reader. In this course, you will learn the inspiration behind deep learning and how it relates to the human brain. You will also gain clear knowledge about the building blocks of neural networks (called neurons) along with how they compute, make predictions, and learn. We will then move on to learning the theory of deep neural networks, including how data is fed into it, how neurons compute the data, and how predictions are made. We'll end the course by learning how deep neural networks learn/train using a combination of feed-forward and back-propagation cycles.

Four pictures were posted recently on Data Science Central, and have immediately become popular. They are designed as one-page tutorials on some specific (basic or advanced) topics. Click on the links below to find those related to the subjects that you are interested in.

Kaggle is a website where you can find competitions to solve data science problems. It's free to join and it gives you the opportunity to practice your skills on real-world datasets in various industries. This post will introduce 10 datasets that are great for practicing your skills before heading into an interview or just because they're interesting! The Titanic dataset is probably one of the most popular datasets on Kaggle. It's a great dataset to start with because it has a lot of Variables (13) and Records (over 1500).

Machine learning (ML) is a part of artificial intelligence (AI) that teaches the computer to work and make decisions based on historical data. A ML algorithm learns from historical data to generate a predictive model used to forecast the future outcome. Advanced forms of ML models could be applied in AI applications, such as Recommender System, Text Processing and Image Recognition. To work with ML, a data scientist should have a good knowledge of mathematics and statistics, and the ability to process data and interpret the results. To process the data, you have to use specific tools or be able to program.

Udemy Coupon - Machine Learning Regression Masterclass in Python, Build 8 Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard English [Auto-generated] Students also bought Deep Learning Prerequisites: Linear Regression in Python Learn Regression Analysis for Business Regression Analysis / Data Analytics in Regression Regression Analysis for Statistics & Machine Learning in R Machine Learning for Beginners: Linear Regression model in R Preview this Course GET COUPON CODE Description Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.