Coursera

4.64

Learners will be able to Identify and manage ethical situations that may arise in their careers. Learnerrs will be able to apply ethical frameworks to help them analyze ethical challenges. Learners will be familiar with...

4.39

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4.75

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You...

4.47

U​se the collaborative Databricks workspace to write scalable Spark SQL code that executes against a cluster of machinesInspect the Spark UI to analyze query performance and identify bottlenecksCreate an end-to-end pipeline that reads data, transform...

4.52

Describe what makes a good or bad visualizationUnderstand best practices for creating basic chartsIdentify the functions that are best for particular problemsCreate a visualization using matplotlb

4.42

Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.

4.49

Welcome to the CLICS-Machine Translation MOOCThis MOOC explains the basic principles of machine translation. Machine translation is the task of translating from one natural language to another natural language. Therefore, these algorithms can help...

4.87

Identify and mitigate privacy and ethical risks in AI projectsApply human-centered design practices to design successful AI product experiencesBuild AI systems that augment human intelligence and inspire model trust in users

3.77

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank...

4.59

Compare static vs. dynamic training and inferenceManage model dependenciesSet up distributed training for fault tolerance, replication, and moreExport models for portability

4.21

Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are...

4.61

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able...

4.74

We will learn computational methods - algorithms and data structures - for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to...

4.54

Gain an understanding of the motivations behind data collection and analysis methods used by marketing professionalsUnderstand frameworks and approaches to measuring consumers’ digital actionsLearn to evaluate and choose appropriate web analytics too...

4.66

This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation,...

4.06

Welcome to Data Analytics Foundations for Accountancy I! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning...

4.61

Generalize a ML model using Regularization techniquesTune batch size and learning rate for better model performanceOptimize a ML modelApply the concepts in TensorFlow code

4.46

Compare the key required aspects of a good featureUnderstand how to preprocess and explore features with Cloud Dataflow and Cloud DataprepCombine and create new feature combinations through feature crossesUnderstand and apply how TensorFlow transform...

4.64

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised...

4.83

This course is for business analysts and SAS programmers who want to learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data. The course builds on the concepts...