Coursera

4.62

Define and discuss big data opportunities and limitations. Work with IBM Watson and analyze a personality through Natural Language Programming (NLP). Examine how AI is used through case studies. Examine and discuss the roles ethics...

4.57

This is the third course of the Advanced Machine Learning on GCP specialization. In this course,We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the...

4.8

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This...

4.13

Know how to operate software that will help you create and run Python code. Execute Python code for wrangling data from different structures into a Pandas dataframe structure. Run and interpret fundamental data analytic tasks...

3.86

L​earn to merge, clean, and manipulate data using Python libraries such as Numpy and PandasG​ain familiarity with the basic and advaned Python functonalities such as importing and using modules, list compreohensions, and lambda functions. S​olve...

4.58

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...

4.74

The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses...

4.18

Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Just as they have done in other research areas, researchers are leveraging deep learning to achieve state-of-the-art results. In...

4.66

BigQuery is Google's fully managed, NoOps, low-cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can...

3.86

Employ artificial intelligence techniques to test hypothesis in PythonApply a machine learning model combining Numpy, Pandas, and Scikit-Learn

4.91

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. ​​ In this course, you will prepare to take the AI-900 Microsoft Azure...

4.87

Natural language processing supports applications that can see, hear, speak with, and understand users. Using text analytics, translation, and language understanding services, Microsoft Azure makes it easy to build applications that support natural...

4.49

Use the basic components of building and applying prediction functionsUnderstand concepts such as training and tests sets, overfitting, and error ratesDescribe machine learning methods such as regression or classification treesExplain the complete pr...

4.75

How to train and develop an image classification system using machine learningHow to train and develop an object detection system using machine learningHow to deploy a machine learning model to a microcontroller

3.55

Understand the applicability, benefits and limits of the crowdsourcing approachIntegrate an on-demand workforce directly into your processes and build human-in-the-loop processesControl the quality and accuracy of data labeling to develop high perfor...

4.54

This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framewor...

4.45

This online course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize...

4.28

Define the characteristics of an analytical and data-driven mindset. Recognize how to establish the objectives and desired outcomes of a data analytics project for a business or client. Recognize ways data is described and interpreted....

4.4

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that...

B​y the end of this course, you will be able to identify the key components of the data mining pipeline ​and describe how they're related. Y​ou will be able to identify particular challenges presented by...