Python is a very popular high-level language created by Guido van Rossum and first released in 1991. Python is named after the greatest comedy act of all time, Monty Python. Python can be used to create pretty much any type of application. Python has been popular for many years and it's popularity shows no signs of stopping anytime soon. Been an in demand language, knowing Python is definitely something beneficial for your career as a software developer. Python is a very widely used programming languages that can do almost anything. Having working knowledge of high level programming languages is something any software developer should have. Whether it is a script you need to run or a complete application, Python is something you can use in your daily life as a programmer. Here's a list of some of the best Python books, courses, videos and tutorials in 2017 to help you learn Python.

About this course: In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.

About this course: This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.

About me @marijnvanzelst Marijn van Zelst • Software engineer at Luminis Amsterdam 3. Goal: • Practical knowledge about machine learning • What kind of problems it can solve • Do your own experiment 4. Why is machine learning interesting for Software Developers? • New opportunities • Builds on existing knowledge • Open-source communities and libraries 5. Tensorflow • Developed by Google • Open-source • Has api's for most major programming languages • High level api 6. Machine learning Learn from examples 7. Handwritten digit classification MNIST dataset 8. What is the difference between a 2 and a 3? 9. Neural networks Image recognition Speech recognition Natural language recognition Churn prediction Fraud detection Anomaly detection 10. Learning algorithm • While not done • pick a training example (input data, class) • run it through the neural network which produces a prediction • modify the weights in the neural network so that the prediction is closer to the actual class 14. Agile approach to machine learning • Theory takes a lot of time to explain and learn • Using high level api's you can do experiments without knowing all the mathematical details • When you have your first results and want to improve • Learn more • Ask for expert help 15. Using a high level api • Which features do I use? Neural nets intuition • Input: • Features (coordinates in feature space) • Output: • A predicted class at every coordinate in the feature space white black black y x 24.

Science has a vital role in shaping our society and economy. The impact of science can continue to grow provided our scientists and science professionals are equipped with skills to create an innovative, sustainable and prosperous future. Too frequently, leadership skills are mistakenly equated with management skills; many only see value in leadership education when people reach senior positions and are managing teams. When leadership education is conceptualized as an action undertaken by many rather than a title held by few, it can increase the quality of people's contribution to their sector. This is because leadership education can provide a pathway for building self-awareness, self-efficacy, interpersonal skills, resilience and adaptability.

About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three.

About this course: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people's opinions and preferences, in addition to many other kinds of knowledge that we encode in text. This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern.

About this course: In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory.

This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service.

In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees. Do I need some Skynet to run it?