Practicing data science and working with real-world data and business problems is rather different than, for instance, building data science projects in Python using toy datasets. While being a part of a data science team in an enterprise, one should expect many challenges, including messy data, lack of data, unclear goals, difficult communication with business managers who want quick results, model performance in production being very different from testing performance, etc. Therefore, to become a successful data scientist with a portfolio of outstanding projects, it is not enough to be good at coding and building machine learning models. One should further be able to approach a project strategically and consider many different factors, not only from the viewpoint of a data scientist but also from a business perspective. However, what if you are actually not interested in succeeding in data science? In that case, read carefully through the tips provided below.

The way businesses communicate internally has evolved rapidly, moving from email and telephone to automated digital communication channels. IT operations teams must meet the demands of digital transformation faster than ever before, with instant 24/7 support and faster ticket resolution. "The product works out of the box. With minimal configuration you'll have exposure over your infrastructure to a level you've not had before, allowing you to make informed business decisions."

In the increasingly competitive and changing world, efficient education system that drives the human development in the country is the key to a nation's progress. The education providers-schools and higher learning institutions must focus on student success and design instruction that considers the individual differences of the learners. In recent years, learning analytics has emerged as a promising area of research that extracts useful information from educational databases to understand students' progress and performance. The term Learning Analytics is defined as the measurement, collection, analysis and reporting of information about learners and their contexts for the purposes of understanding and optimizing learning. As the amount of data collected from the teaching-learning process increases, potential benefits of learning analytics can be far reaching to all stakeholders in education including students, teachers, leaders and policy makers.

Artificial Intelligence will define the next generation of software solutions. This computer science course provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people's lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI. Discover how machine learning can be used to build predictive models for AI. Learn how software can be used to process, analyze, and extract meaning from natural language; and to process images and video to understand the world the way we do.

Learn AI theory and follow hands-on exercises with our free courses from the Intel AI Academy for software developers, data scientists, and students. These lessons cover AI topics and explore tools and optimized libraries that take advantage of Intel processors in personal computers and server workstations.

This is the age of data! As soon as you open your Facebook account, you are inundated with huge amount of data. You get to see posts from your friends, which could be in the format of text, pictures and videos. Now, just imagine if you could tap into this data and use it gain insights, that would be just wonderful, wouldn't it? And this is exactly where data science comes in.

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

Learn how to use TensorFlow 2.0 in this full course from Tech with Tim. This course will demonstrate how to create neural networks with Python and TensorFlow 2.0. You can watch the full video on the freeCodeCamp.org

Below is the 15 best machine learning course to accelerate your ML journey this year. The holy grail of machine learning online course, Machine Learning by Stanford is considered as the best machine learning course by many. This course is prepared and maintained by Andrew Ng, pioneer machine learning scientist who've led ML research projects for both Google and Chinese giant Baidu. Although the course requires a paid subscription, you can ask for financial aid if you're a student. This online machine learning course from DataCamp is the best machine learning course with a primary emphasis on statistics – the de facto requirement for effective data science projects.

Three months go by quick. In that time, I, your humble Content Manager, have had the chance to work with some incredibly bright people. It was intimidating at first: coming into work every morning and seeing whiteboards covered in arcane equations and incomprehensible diagrams. I knew what we did in principle when I joined Acerta--we're using AI to help automakers get products to market faster and with fewer defects--but I certainly didn't understand what that actually involves. Over the past three months, I've had crash courses in automotive manufacturing, cloud migration, data quality, fleet maintenance, machine learning, software internships, and statistical process control, among other things.