Coursera

4.41

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals...

3.92

No doubt working with huge data volumes is hard, but to move a mountain, you have to deal with a lot of small stones. But why strain yourself? Using Mapreduce and Spark you tackle the...

4.63

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them....

4.79

In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement...

3.96

This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk...

4.71

Explain the capstone project scenario and a problem that you will solve with location data. Select a location data provider based on its features. Demonstrate proficiency by creating a Jupyter Notebook for the capstone project...

4.7

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope...

4.5

Describe Data Science’s role in various contextsUnderstand how Statistics and Machine Learning affect Data ScienceUse the key terms used by data scientistPredict whether a Data Science project will be successful

4.76

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate...

4.78

Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming. Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentat...

4.7

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an...

4.01

Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility. Define quantitative trading and the main types of quantitative trading strategies. Understand the basic steps in exchange arbitrage, statisti...

4.72

Make use of unicode characters and stringsUnderstand the basics of building a search engineSelect and process the data of your choiceCreate email data visualizations

4.42

Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you...

4.71

Use dimensions, measures, filters, and pivots to analyze and visualize dataCreate advanced metrics instantaneously with table calculationsCreate and share visualizations using Looks and dashboardsUtilize folders and boards in Looker to manage and org...

4.57

Apply basic data science techniques using PythonUnderstand and apply core concepts like Data Frames and joining data, and use data analysis libraries like pandas, numpy, and matplotlibDemonstrate how to load, inspect, and query real-world data,...

4.75

This course will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox"...

4.44

This four-module course introduces users to Julia as a first language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications...

4.69

Walk through examples of prognostic tasksApply tree-based models to estimate patient survival ratesNavigate practical challenges in medicine like missing data

4.28

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.