Course directory

Get familiar with the basics of Machine Learning.

I will show you how you can get started on your Data Science Journey and Data Science Career by starting this Virtual Internship (Online Internship) anywhere in the world with KPMG Data Analytics Team.

Machine learning is a field of study in computer science. This course introduces students to the algorithms and statistical models that help computer systems learn to perform a task without specific instructions and automatically improve their performance through data/experience.

This video introduces the topic of recurrent neural networks and the Elman Cell.

We examine the very important, yet often misunderstood topic, of sampling distributions. When many samples are taken from a population those sample means form their own distribution.

I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before.

Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming.

What is Pandas ? Why to use Pandas? Key Features of Pandas, Pandas with other Libraries

As a data scientist, we are known to crunch numbers, but what happens when we run into text data? In this tutorial, I will walk through the steps to turn text data into a format that a machine can understand, share some of the most popular text analytics techniques, and showcase several natural language processing (NLP) libraries in Python including NLTK, TextBlob, spaCy and gensim.

This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners tutorial video:

We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets.

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

In this Python Tutorial for Beginners, we will be learning the fundamentals of Python programming from the ground up. We will be learning how to work with basic data types, write conditionals, write for/while loops, write functions, import modules also learn closures, decorator & generator and explore the standard library.

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning. In this lecture series, research scientists from leading AI research lab, DeepMind, delivered 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.