Introduction to Machine Learning Course

Introduction to Machine Learning Course

What you will learn

  • Learn what Machine Learning is and meet Sebastian Thrun!
  • Find out where Machine Learning is applied in Technology and Science.
  • Use Naive Bayes with scikit learn in python.
  • Splitting data between training sets and testing sets with scikit learn.
  • Calculate the posterior probability and the prior probability of simple distributions.
  • Learn the simple intuition behind Support Vector Machines.
  • Implement an SVM classifier in SKLearn/scikit-learn.
  • Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels.
  • Learn the formulas for entropy and information gain and how to calculate them.
  • Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.
  • Understand how continuous supervised learning is different from discrete learning.
  • Code a Linear Regression in Python with scikit-learn.
  • Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
  • Remove outliers to improve the quality of your linear regression predictions.
  • Identify the difference between Unsupervised Learning and Supervised Learning.
  • Implement K-Means in Python and Scikit Learn to find the center of clusters.
  • Apply your knowledge on the Enron Finance Data to find clusters in a real dataset.
  • Understand how to preprocess data with feature scaling to improve your algorithms.

This Course will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.

This course is also a part of our Data Analyst Nanodegree.

How to Enroll Introduction to Machine Learning Course course?

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  • How many members can access this course with a coupon?

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