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Fundamentals of Reinforcement Learning

Fundamentals of Reinforcement Learning

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning.

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  • 15 hours
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The Structured Query Language (SQL)

The Structured Query Language (SQL)

In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database.

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  • 55 hours
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Business Statistics and Analysis Capstone

Business Statistics and Analysis Capstone

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 publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst. Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand. Please remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses.

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  • 7 hours
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Introduction to Applied Machine Learning

Introduction to Applied Machine Learning

This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications.

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  • 7 hours
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Text Retrieval and Search Engines

Text Retrieval and Search Engines

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.

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  • 31 hours
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Statistical Inference and Hypothesis Testing in Data Science Applications

Statistical Inference and Hypothesis Testing in Data Science Applications

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values.

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  • 37 hours
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Predictive Modeling and Analytics

Predictive Modeling and Analytics

Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business.

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  • 11 hours
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Getting Started with SAS Visual Analytics

Getting Started with SAS Visual Analytics

In this course, you learn more about SAS Visual Analytics and the SAS Viya platform, how to access and investigate data in SAS Visual Analytics, and how to prepare data for analysis using SAS Data Studio.

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  • 5 hours
  • English
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Practical SAS Programming and Certification Review

Practical SAS Programming and Certification Review

In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.

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  • 21 hours
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Measurement Systems Analysis

Measurement Systems Analysis

In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability.

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  • 17 hours
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Analysis of Business Problems

Analysis of Business Problems

When does an opportunity to increase the bottom line become a liability for long-term brand sustainability and profitability? That is the question that GAS GAS, an off-road motorcycle manufacturer, is confronting. In this culminating course, it’s time to use the business tools you have learned throughout the specialization to solve this real business problem. To help you as you develop a solution to the GAS GAS dilemma, in the Capstone you will also learn a six-step analysis of business problems methodology.

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  • 10 hours
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Sample-based Learning Methods

Sample-based Learning Methods

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning.

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  • 22 hours
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Big Data Modeling and Management Systems

Big Data Modeling and Management Systems

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples.

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  • 13 hours
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Machine Learning: Classification

Machine Learning: Classification

Case Studies: Analyzing Sentiment & Loan Default Prediction In 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,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

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  • 21 hours
  • English
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Linear Regression for Business Statistics

Linear Regression for Business Statistics

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 is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects.

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  • 28 hours
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Marketing Analytics

Marketing Analytics

Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI).

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  • 16 hours
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Advanced Machine Learning and Signal Processing

Advanced Machine Learning and Signal Processing

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<\n\nThis course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines.

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Machine Learning With Big Data

Machine Learning With Big Data

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data.

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  • 22 hours
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Cluster Analysis in Data Mining

Cluster Analysis in Data Mining

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

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  • 17 hours
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Applied Machine Learning in Python

Applied Machine Learning in Python

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled.

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  • 31 hours
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Applied Social Network Analysis in Python

Applied Social Network Analysis in Python

This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

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  • 26 hours
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Business Metrics for Data-Driven Companies

Business Metrics for Data-Driven Companies

In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognize the most critical business metrics and distinguish them from mere data. You’ll get a clear picture of the vital but different roles business analysts, business data analysts, and data scientists each play in various types of companies.

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  • 8 hours
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Increasing Real Estate Management Profits: Harnessing Data Analytics

Increasing Real Estate Management Profits: Harnessing Data Analytics

In this final course you will complete a Capstone Project using data analysis to recommend a method for improving profits for your company, Watershed Property Management, Inc. Watershed is responsible for managing thousands of residential rental properties throughout the United States. Your job is to persuade Watershed’s management team to pursue a new strategy for managing its properties that will increase their profits.

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  • 23 hours
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3D Data Visualization for Science Communication

3D Data Visualization for Science Communication

This course is an introduction to 3D scientific data visualization, with an emphasis on science communication and cinematic design for appealing to broad audiences.

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  • 32 hours
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Business Analytics for Decision Making

Business Analytics for Decision Making

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 the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions. A key element of decision making is to identify the best course of action. Since businesses problems often have too many alternative solutions, you will learn how optimization can help you identify the best option.

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  • 9 hours
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