Predictive Analytics 1 – Machine Learning Tools Using Python

Expand your AI, machine learning and data science knowledge by learning about the central paradigms of predictive analytics—classification and prediction—using the programming language, Python.

Enroll in Predictive Analytics 1

About This Online Course

Continue to expand your knowledge about artificial intelligence, machine learning and data science tools by understanding the basic concepts in predictive analytics—the most prevalent form of data mining—using Python.

Python is the most popular programming language in the world. It is very powerful and very flexible language that enjoys a very strong user community, and is often utilized in AI, machine learning and data science applications.

This online training course from statistics.com covers the two core paradigms that account for most public- and private-sector applications of predictive modeling: classification and prediction. You will study commonly used machine learning techniques and learn how to combine models to obtain optimal results.

By the end of the course you will be able to visualize and explore data, provide an assessment basis for predictive models and choose appropriate performance measures. You will also become familiar with common algorithms including k-nearest neighbor, Naive Bayes, classification and regression trees, as well as ensemble models.

This online course is especially useful if you want to understand what predictive modeling might offer to your organization, undertake pilots with minimum setup costs, oversee predictive modeling projects or work with consultants/technical experts regarding predictive modeling deployments.

This online training course utilizes Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, 1st edition (Wiley 2019), Shmueli, G., Bruce, P. C., Gedeck, P., and Patel, N. R. (available on the Wiley website). Learners must purchase the book before starting the course.

What You Will Learn

  • Visualize and explore data to better understand relationships among variables
  • Organize the predictive modeling task and data flow
  • Develop machine learning models with the KNN, Naive Bayes and CART algorithms using Python’s scikit-learn
  • Assess the performance of these models with holdout data
  • Apply predictive models to generate predictions for new data
  • Use Python's sci-kit learn package to implement the models in the course

Your Instructor

Kuber Deokar is instructional operations supervisor at statistics.com where he is responsible for coordination of online courses and seamless interactions between the management team, course instructors, teaching assistants and students. Mr. Deokar also serves as senior teaching assistant and shares instructional responsibilities for several courses. He handles consultancy assignments from the statistics.com office in Pune, India.

Mr. Deokar holds Master and Bachelor of Science degrees in Statistics from the University of Pune, India, where he also taught undergraduate statistics.

Who Should Take This Course

This course is designed for marketing and information technology managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.

Prerequisites

None.

You should be sufficiently familiar with Python to follow and are well-versed in statistics, or have the equivalent understanding of topics covered in the statistics.com courses: Statistics 1 – Probability and Study Design course and Statistics 2 – Inference and Association.

After finishing this course, you can take two other statistics.com courses to continue to grow your knowledge:

Course Certificate

A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.

In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.

Course Format

This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. Participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Course Pricing

$599 (per person)

Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.

Continuing Education Unit Credits

This online course provides 5.0 CEUs upon 50-percent completion.

This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.