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About the Program

Cognitive analytics is all about unlocking the hidden insights from your data. Data can be structured, unstructured, audio, or visual – and to make all that data work together as an engine that augments human ability. In this learning path, you will learn how to use state-of-the-art analytic tools from IBM to accelerate your business. You will learn how to use statistical concepts to describe, explore, and understand your data. You will also go through modeling approaches from basic machine learning concepts to advanced algorithms and optimizations. Get some practical knowledge with open source Machine Learning libraries like Apache SystemML, and learn how to use the most popular language by data scientists for machine learning: Python. At the end, learn how to use Watson Analytics, a smart and fast service that uses the power of IBM’s Watson to automatically discover insights in your data.

Cognitive Analytics with IBM - Course Outline

Course 1: Statistics 101

Effort: 6 hours Level: Beginner

Take this course and you won't fail statistics. Welcome to the Statistics 101 course, taught by Murtaza Haider, Associate Professor at Ryerson University. Statistics is one of the most challenging topics to learn, but Murtaza brings a gentle introduction to statistics in practice. Learn about descriptive statistics, variance, probability, correlation, and data visualization. This course ends with a fully-guided statistics exercise exploring the “hot” topic of: do good looking professors get better teaching evaluations? A free trial of SPSS Statistics is included in this course.

Course 2: Predictive Modeling Fundamentals I

Effort: 5 hours Level: Intermediate

This course provides an introduction to predictive modeling fundamentals. You will learn predictive modeling techniques using a real-world data set and also get introduced to IBM's popular predictive analytics platform IBM SPSS Modeler.

Course 3: Machine Learning with Python

Effort: 12 hours Level: Intermediate

Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

Course 4: Machine learning with Apache SystemML

Effort: 8 hours Level: Intermediate

Apache SystemML is a declarative style language designed for large-scale machine learning. It provides automatic generation of optimized runtime plans ranging from single-node, to in-memory, to distributed computations on Apache Hadoop and Apache Spark. SystemML algorithms are expressed in R-like or Python-like syntax that includes linear algebra primitives, statistical functions and ML-specific constructs.

Learning Path Courses

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    Statistics 101

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    Predictive Modeling Fundamentals I

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    Machine Learning with Python

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    Machine learning with Apache SystemML

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