Coursera

4.06

Conducting Hypothesis Tests in PythonApplications of Hypothesis Testing in the WorkplaceConducting Hypothesis Tests in Excel

3.81

This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets...

4.91

Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a...

4.76

In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to...

4.59

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototy...

4.73

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute...

4.34

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in...

4.71

Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic...

4.47

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also al...

4.67

In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine...

4.43

Define Artificial Intelligence and Machine LanguageDescribe AI tools and roles, and the Microsoft Team Data Science Process Work with Azure APIs, including those for vision, language, and searchCreate, train, test and deploy your AI model...

4.75

Conduct a factorial experiment in blocks and construct and analyze a fractional factorial designApply the factorial concept to experiments with several factorsUse the analysis of variance for factorial designsUse the 2^k system of factorial designs

4.53

Create bar charts, histograms, pie charts, scatter plots, line graphs, box plots, and maps using R and related packages. Design customized charts and plots using annotations, axis titles, text labels, themes, and faceting. Create maps...

3.8

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape...

4.4

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify...

4.72

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from...

4.72

To describe the data structure needed for network analysis, path analysis, and text analytics. How to create network analysis to analyze relationship between entities in SAS Visual Analytics. How to create path analysis to understand...

4.25

How to perform data engineering with Azure Synapse Apache Spark Pools to boost the performance of big-data analytic applications.

4.6

Describe how each type of clinical data are generated, specifically outlining who creates the data, when and why the data are generated. Write SQL code to combine two or more tables using database joins. Write...

4.63

Develop a project proposal Assess the quality of the data and perform exploratory analysis Create KPIs and dashboards and assess your analysis Create your data story and write a narrative to accompany your visualization