Over the years, almost every organization has understood the importance of analyzing data. In fact, it would not be an overstatement to say that "No organization will be able to survive today's cut-throat competition if it does not analyze data." Data analysis as we know it is the process of taking the source data, refining it to get useful information, and then making useful predictions from it. In this Learning Path, we will learn how to analyze data using the powerful toolset provided by Python. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Jack Ma, who is the founder CEO of Internet giant Alibaba, warns that If we do not change the way we teach, 30 years down the line we will be in trouble. The things we teach our children are things from the past 200 years- it's knowledge-based. And we can not teach our kids to compete with machines, that are smarter. This man is a big employer and his assertion on what the employers would need in the near future is credible enough, at least to me. As I said earlier that there's no way we can compete with AI when it comes to efficiency, knowledge, accuracy and incessant replication; how else can we beat it?

Human Resources (HR) have been experiencing significant changes thanks to the evolution of information technologies in the last two decades. Today, Artificial Intelligence (AI) is reshaping the way that companies manage their workforce and make HR plans, which increases productivity and employee engagement in general. Bearing in mind that employee engagement programs boost company revenue by 26%, it is clear that you should accept AI solutions to strengthen your team and gain some long-term benefits. In this article, we will present you 5 ways to use AI in human resources management. AI has the power to take your HR experience to the higher level.

A Montreal program is trying to help get more women working in artificial intelligence. Twenty-eight women, mostly senior undergraduate students, began the six-week AI4Good summer lab on May 14. Almost none of the participants have any experience with AI or machine learning, said Doina Precup, one of the program's creators. The program, now in its second year, aims to give participants the background and the confidence to continue in AI, whether that's by studying it in graduate school, applying the technology in other areas or by starting a business, said Precup, a computer science professor at McGill University and the head of Google-affiliated AI company DeepMind's Montreal lab. In 2017, only 20 per cent of tech workers at Google were women, according to the company's diversity report. Other large tech companies have reported similar numbers.

TensorFlow is quickly becoming the technology of choice for machine learning, because of its ease to develop intelligent machine learning applications and powerful neural networks. If you're a data professional who is familiar with Python and wants to use TensorFlow for performing machine learning activities on a day-to-day basis, then go for this learning path. This comprehensive 2-in-1 course gives you a clear understanding of machine learning models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with interesting examples and real-world use cases. It's a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to develop efficient and intelligent applications based on your understanding of the different machine learning concepts with every section.

Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered in many computer science and software engineering degree programs. Even if it is, it may be taught in a bottom-up, theory-first manner, making it unclear which parts are relevant on a given project. In this post, you will discover some top introductory books to statistics that I recommend if you are looking to jump-start your understanding of applied statistics. I own copies of all of these books, but I don't recommend you buy and read them all.

Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions). Learn why it's useful and how to approach the problem. There are Rule-Based and ML-Based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set.

Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard.

The things that Mike taught are practical and can be applied in the real world immediately." This is the very FIRST course in a series of courses that will focus on NLTK. Natural Language ToolKit (NLTK) is a comprehensive Python library for natural language processing and text analytics. Note: This isn't a modeling building course. This course is laser focused on a very specific part of natural language processing called tokenization.

The core graph program extraction algorithm, automatic differentiation, and Python language interoperability features of Swift for TensorFlow can be implemented for other programming languages, and we are occasionally asked why we didn't use some other one for this project. The engineers on the project were previously familiar with Swift (and several other languages), but the choice was guided by the goals of our project, which imposed specific technical requirements (explained below). This choice was also discussed extensively, debated with coworkers and other interested engineers, and we concluded that Swift was the best direction. In this document we're sharing our deliberation process with the community to help explain our decisions. That said, while our choice of language was guided by our specific project goals, we would love to see wider application of these techniques and ideas in the context of other programming languages! If you are interested in pursuing a similar project, please reach out to us and we will happily share our expertise. As discussed in the design overview document our project goal is to improve usability of TensorFlow. We quickly realized that our core static analysis-based Graph Program Extraction algorithm would not work well for Python given its highly dynamic nature. This led us down the path of having to pick another language to work with, and we wanted to approach this methodically. As such, we defined goals for the project, explored which properties of a programming language are important to achieve those goals, and then evaluated a lot of languages against these properties. You already know the outcome--we eventually settled on Swift.