Coursera

3.28

1. The PyMC3/ArViz framework for Bayesian modeling and inference2. Build real-world models using PyMC3 and assess the quality of your models

3

1. Markov Chain Monte Carlo algorithms2. Implementing the above in Python3. Assess the performance of Bayesian models

4

The course is devoted to the usage of computer vision libraries like OpenCV in 2d image processing. The course includes sections of image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, video tracking....

4.78

In this course, you learn about the data structure needed for geographic mapping and forecasting, how to use SAS Data Studio to restructure data for analysis, and how to create geo maps and forecasts in...

3.94

The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one...

4.24

Welcome to the Reinforcement Learning online course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included - using deep neural networks...

4.22

In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be...

4.91

Development of an analytic mindset for approaching business problems. The ability to appraise the value of datasets for addressing business problems using summary statistics and data visualizations. Competence in operating business analytic software...

4.39

This is the third course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow...

4.18

This is the second course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow...

4.2

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow...

4.69

Case Studies: Analyzing Sentiment & Loan Default PredictionIn our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,. ....

4.6

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn...

4.77

AI Ethics research is an emerging field, and to prove our skills, we need to demonstrate our critical thinking and analytical ability. Since it's not reasonable to jump into a full research paper with our...

Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just...

4.3

Develop data strategy and process for how data will be generated, collected, and consumedLoad and process formatted datasets such as CSV and JSON. Deal with data in various formats (e. g. timestamps, strings) and filter...

4.5

In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given...

4.62

In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while...

4.68

Conduct experiments w/computer models and understand how least squares regression is used to build an empirical model from experimental design dataUnderstand the response surface methodology strategy to conduct experiments where system optimization i...

4.8

Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model...