This course teaches and demonstrates some of the most basic, yet crucial concepts in Data Science and Analytics.

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

If recent clickbait headlines are to be believed, robots are already taking over our schools, relegating "Sir" or "Miss" to the status of a second-rate computer dumped at the back of the class. Yet to many experts, the real value of artificial intelligence (AI) to education may be far more humdrum as a back-of-house tool to free up time for human teachers to build students' social skills, resilience, appetite for learning and character. Miles Berry, principal lecturer in computing education at the University of Roehampton and a key architect of the national curriculum for computing, introduced to replace ICT four years ago, is disappointed at how few schools have exploited the new programme fully. "AI is difficult to teach and schools either lack relevant resources or don't know how to apply them, but in order to plug the technology skills gap, we must give our youngsters time to experiment with creating rudimentary chatbots for example," he says. "Setting up a Google Assistant, Apple Siri or Amazon Alexa and getting it to answer some of the questions that come up in a lesson would be a fairly simple task for many computing teachers, but to get them on-side, we need to talk far more about the role of machine-learning and far less about the dawn of the robots."

Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

In this post, you will learn about different types of test cases that you could come up for testing features of the Data Science/Machine Learning models. Testing features are one of the key sets of which needs to be performed for ensuring the high performance of Machine Learning models in a consistent and sustained manner. Features make the most important part of a Machine Learning model. Features are nothing but the predictor variable, which is used to predict the outcome or response variable. Simply speaking, the following function represents y as the outcome variable and x1, x2, and x1x2 as predictor variables.

Leverage Machine Learning and TensorFlow in Python to improve your business! The best job to have in 2017 according to Glassdoor? The #1 skill you need to start a career in Data Science? So, if you are interested in a career in data science, algorithmic trading, robotics, or any industry where human labor is getting replaced by machines, you have come to the right place! We have prepared an amazing course not only to get you acquainted with, but help you understand how deep machine learning works!

In this tutorial you will learn how to install OpenCV 4 on your Ubuntu system. OpenCV 4 has not been officially released yet; however, a release is expected in autumn 2018. In the meantime, we can compile and install OpenCV 4 from source using the pre-release on GitHub. Once OpenCV 4 is officially released I will update this blog post as well. So, why bother installing OpenCV 4? You may want to consider installing OpenCV 4 for further optimizations, C 11 support, more compact modules, and many improvements to the Deep Neural Network (DNN) module.

Learn to use functions and apply Codes. This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . 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.

Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. Using Spark, we can create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. In this course, you'll learn how to use the Spark MLlib. You'll find out about the supervised and unsupervised ML algorithms. You'll build classifications models, extracting proper futures from text using Word2Vect to achieve this.

Although there are hundreds of statistical hypothesis tests that you could use, there is only a small subset that you may need to use in a machine learning project. In this post, you will discover a cheat sheet for the most popular statistical hypothesis tests for a machine learning project with examples using the Python API. Note, when it comes to assumptions such as the expected distribution of data or sample size, the results of a given test are likely to degrade gracefully rather than become immediately unusable if an assumption is violated. Generally, data samples need to be representative of the domain and large enough to expose their distribution to analysis. In some cases, the data can be corrected to meet the assumptions, such as correcting a nearly normal distribution to be normal by removing outliers, or using a correction to the degrees of freedom in a statistical test when samples have differing variance, to name two examples.