Intro to Machine Learning – Data Science Course by Udacity

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Level: Beginner
Duration: 10 weeks
Delivery: Online
Certification: Yes
Cost: 0
Course Provider: Udacity

Overview

This course will teach you to analyse data using machine learning techniques, and prepare you for the Data Analyst Nanodegree. You’ll learn how to use tools such as pre-written algorithms and libraries to answer interesting questions.

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

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 Udacity’s Data Analyst Nanodegree.

In this course, you’ll learn by doing. Machine learning will come to life by showing you fascinating use cases and tackling interesting real-world problems like self-driving cars. For your final project you’ll mine the email inboxes and financial data of Enron to identify persons of interest in one of the greatest corporate fraud cases in American history.

Training Course Content

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.

Who Is It For?

This course is not for absolute beginners. To succeed, you must be proficient at programming in Python and basic statistics. Also, a familiarity with Data Science will get you familiar with scientific problem-solving. You should take this course only if you are familiar with these topics and wish to move on with a career in ML.

Cost

0

About the Provider

Katie Malone and Sebastian Thrun teach this course for Udacity.

Udacity began as an experiment in online learning, when Stanford instructors Sebastian Thrun and Peter Norvig elected to offer their “Introduction to Artificial Intelligence” course online to anyone, for free. Over 160,000 students in more than 190 countries enrolled. The potential to educate at a global scale was awe-inspiring, and Udacity was founded to pursue a mission to democratize education. It would take several years of intensive iteration and experimentation to clarify our focus on career advancement through mastery of in-demand skills, but today, Udacity proudly offers aspiring learners across the globe the opportunity to participate in—and contribute to—some of the most exciting and innovative fields in the world.

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User Reviews

Kai
· October 31, 2018

At no cost, you enter into a pretty solid 10 week program. Nothing groundbreaking, but it'll help you on your journey.

Kam
· November 8, 2018

Ok for a free course.

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