Introduction to Machine Learning Course (Udacity)

Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.
This is a class that 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.

Syllabus

LESSON 1
Welcome to Machine Learning

• Learn what Machine Learning is and meet Sebastian Thrun!
• Find out where Machine Learning is applied in Technology and Science.

LESSON 2
Naive Bayes

• 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.

LESSON 3
Support Vector Machines

• 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.

LESSON 4
Decision Trees

• Code your own decision tree in python.
• Learn the formulas for entropy and information gain and how to calculate them.
• Implement a mini project where you identify the authors in a body of emails using a decision tree in Python.

LESSON 5
Choose your own Algorithm

• Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.

LESSON 6
Datasets and Questions

• Apply your Machine Learning knowledge by looking for patterns in the Enron Email Dataset.
• You'll be investigating one of the biggest frauds in American history!

LESSON 7
Regressions

• 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.

LESSON 8
Outliers

• Remove outliers to improve the quality of your linear regression predictions.
• Apply your learning in a mini project where you remove the residuals on a real dataset and reimplement your regressor.
• Apply your same understanding of outliers and residuals on the Enron Email Corpus.

LESSON 9
Clustering

• 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.

LESSON 10
Feature Scaling

• Understand how to preprocess data with feature scaling to improve your algorithms.
• Use a min mx scaler in sklearn.