Introduction to Machine Learning: Supervised Learning

This course is part of Machine Learning: Theory and Hands-on Practice with Python Specialization

Instructor: Geena Kim

What you'll learn

  •   Use modern machine learning tools and python libraries.
  •   Compare logistic regression’s strengths and weaknesses.
  •   Explain how to deal with linearly-inseparable data.
  •   Explain what decision tree is & how it splits nodes.

    • Skills you'll gain

  •   Mathematical Modeling
  •   Decision Tree Learning
  •   Performance Tuning
  •   Data Science
  •   Python Programming
  •   Supervised Learning
  •   Machine Learning Algorithms
  •   Feature Engineering
  •   Classification And Regression Tree (CART)
  •   Scikit Learn (Machine Learning Library)
  •   Exploratory Data Analysis
  •   Statistical Programming
  •   Applied Machine Learning
  •   Machine Learning
  •   Regression Analysis
  •   Matplotlib
  •   Random Forest Algorithm
  •   Data Cleansing
  •   Applied Mathematics
  •   Predictive Modeling

    • There are 6 modules in this course

    Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary. College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

    Multilinear Regression

    Logistic Regression

    Non-parametric Models

    Ensemble Methods

    Kernel Method

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