Introduction to Machine Learning Course
Last Updated on January 7, 2021
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.
