Python Machine Learning Projects
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Python Machine Learning Projects

Get up-and-running via Machine Learning with Python's insightful projects
3.5 (18 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
387 students enrolled
Created by Packt Publishing
Last updated 1/2017
English
Curiosity Sale
Current price: $10 Original price: $125 Discount: 92% off
30-Day Money-Back Guarantee
Includes:
  • 3 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Explore and use Python's impressive machine learning ecosystem
  • Successfully evaluate and apply the most effective models to problems
  • Learn the fundamentals of NLP—and put them into practice
  • Visualize data for maximum impact and clarity
  • Deploy machine learning models using third-party APIs
  • Get to grips with feature engineering
View Curriculum
Requirements
  • This video is a combination of six independent projects, each taking a unique dataset, a different problem statement, and a different solution.
Description

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases. By the end of the course, you will have learned to apply various machine learning algorithms and will have mastered Python's packages and libraries to facilitate computation. You will be able to implement your own machine learning models after taking this course.

About The Author

Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.

Who is the target audience?
  • This video appeals to those developers who have a basic machine learning knowledge and want to explore the various arenas of machine learning by creating insightful and interesting projects.
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Curriculum For This Course
26 Lectures
02:56:22
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Build an App to Find Cheap Airfares
6 Lectures 34:43

This video gives an overview of the entire course.

Preview 02:37

We need the air pricing data from a website to work with. You will learn to do that in this section. 

Sourcing Airfare Pricing Data
03:45

After determining the source of the data, we need to retrieve the data. 

Retrieving the Fare Data with Advanced Web Scraping Techniques
05:05

DOM is the structure of elements that form the web page. We need to get some details of the structure by parsing it. 

Parsing the DOM to Extract Pricing Data
16:17

To get real-time alerts when a particular event occurs, we need to use IFTTT. 

Sending Real-Time Alerts Using IFTTT
03:42

To deploy our app, we'll move on to working in a text editor. You will put together the entire code to get the final result. 

Putting It All Together
03:17
+
Forecast the IPO Market Using Logistic Regression
4 Lectures 32:19

Before deciding strategies for the IPO market, we need to study the IPO market and derive inferences from it.

Preview 12:59

The consideration and inclusion of all factors affecting the market is called feature engineering. Modeling this is as important as the data used in building the model. 

Feature Engineering
08:18

Instead of giving the value of the return, you can predict the IPO for a trade you will buy or not buy. The model used is logistic regression. 

Binary Classification
06:08

It is important to know which features will make the offering successful. You can find that out in this section. 

Feature Importance
04:54
+
Create a Custom Newsfeed
6 Lectures 35:54

To create a model, we have to first have a training dataset. We will use the pocket app for this. 

Preview 09:10

You can't move forward with just the URLs of the stories. You would need the full article. So let's check out how to do that in this video. 

Using the embed.ly API to Download Story Bodies
03:08

Machine learning models work on numerical data. So we will need to transform our text into numerical data using NLP. 

Natural Language Processing Basics
07:23

You will learn about the linear support vector machine in this video. The SVM algorithm separates data points linearly into classes. 

Support Vector Machines
03:40

We have provided a training dataset. But we also need a stream of articles as a testing dataset to run our model against. 

IFTTT Integration with Feeds, Google Sheets, and E-mail
08:10

It would make life easier if you get a personalized e-mail of your stories, right? So you will learn how to do that in this video. 

Setting Up Your Daily Personal Newsletter
04:23
+
Forecasting the Stock Market with Machine Learning
4 Lectures 30:10

Research is the most important thing before we start working on designing a strategy. 

Preview 05:37

Once you have studied the various aspects of the market, it is time to develop a trading strategy. You will learn it in this video. 

Developing a Trading Strategy
12:01

Now that we have our baseline, we will build our first regression model for prediction of stocks. 

Building a Model and Evaluating Its Performance
06:58

Another algorithm to work with is dynamic time warping. It provides us a metric which will inform us about the similarity between two time series. 

Modeling with Dynamic Time Warping
05:34
+
Build an Image Similarity Engine
4 Lectures 26:36

It is very important to understand machine learning's concepts before working with it. 

Preview 04:49

In order to work with images, we need to transform them into a matrix form, that is, numerical form. 

Working with Images
04:16

We will use algorithms to find similar images in the database. 

Finding Similar Images
07:37

We will combine what we have studied so far to build an image similarity engine. 

Building an Image Similarity Engine
09:54
+
Building a Chatbot
2 Lectures 16:40

Design of chatbots consists of parameters like mode of communication, the content, and so on. You will look at that in this video. 

Preview 06:38

Having looked at the working of a chatbot, we will now build a chatbot. 

Building a Chatbot
10:02
About the Instructor
Packt Publishing
3.9 Average rating
7,336 Reviews
52,355 Students
616 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.