Applied machine learning for Everyone
5.0 (6 ratings)
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Applied machine learning for Everyone

apply machine learning techniques and make a final prediction
5.0 (6 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.
116 students enrolled
Created by Daniel We
Last updated 6/2017
English
Current price: $10 Original price: $95 Discount: 89% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 1.5 hours on-demand video
  • 5 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Know and apply solutions for a small dataset
  • Being able to use text in machine learning
  • Apply a solution for deciding which features to use
  • Know a way to handle missing data
  • Finally apply a machine learning algorithm in practice, test it's performance and make your own prediction
View Curriculum
Requirements
  • You should be familiar with python syntax
  • For coding along you need to install certain modules (e.g pandas, scikitlearn, numpy,keras)
  • You should be willing to dive into something new
  • The course is designed for practice not theory!
  • It is definitely useful to first join my Machine Learning for Beginners course but not mandatory
Description

python machine learning ai ? What? How to?...

Machine Learning is currently one of the hottest topics out there. From movie recommendation systems to self driving cars - technology shifts towards ML. Google, Amazon, Microsoft and co. all applying machine learning algorithms to create new products and better serve their customers. The working place of tomorrow is related to ML. No wonder that interest has drastically risen. The difficult question for beginners is how to get into it. From my personal experience the best way is to get one’s hands dirty and apply machine learning in practice. Instead of spending long hours and try to understand all theory behind it, it’s much better to simply execute it because at the end of the day, it boils down to that.

This course addresses "advanced beginners" and is all about executing machine learning in python. We cover some theory along the way but mostly focus on applying it.

If you want to start in machine learning I would recommend checking out my course "Machine learning for Beginners" first, since it covers more basics. However it is not mandatory.

Note that all my courses require to understand at least some basics of machine learning and are hands on practical coding courses. If that's your way of learning it, than this course is for you!

Enough said. We got a lot to do. See you in the first lecture.

best

Daniel

Who is the target audience?
  • "advanced beginners" in machine learning
  • students who joined my other courses and want to extend their machine learning warchest
  • students who prefer a hands on approach for learning
  • all people who want to dive into one of the hottest topics out there
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Curriculum For This Course
11 Lectures
01:41:56
+
Machine learning for Everyone. Start here!
11 Lectures 01:41:56

What are you going to learn in this course. Check out here!

Preview 01:56

Data shortage is a common problem in machine learning. Here you are going to learn how you can create more data

Data shortage. I need more data but how?
14:54

Machine algorithms require numerical data. How can you use string values in machine learning?

Ways to deal with Strings in machine learning
11:32

A common tool to get much better results in machine learning

How to improve your algorithm's result
10:51

Learn and apply a way to avoid overfitting and reduce the features when training your algorithm

Feature selection - which one to choose?
13:53

The more you know how to handle strings the better your warchest

Additional ways to deal with string values
04:50

Learn it apply it!

A practical way to deal with text in scikitlearn
11:15

The introduction and first preprocessing steps. Understand what we are dealing with here

Preview 12:39

The second step of preprocessing for making the final prediction

The final part 2 - Deal with the missing data problem
09:09

Preprocessing, Training and Testing - all right. Now it's time to make your own prediction!.

The final part 3 - Make the prediction - Did I survive?
10:12

Wrap up and final words to support your journey

Log out
00:45
About the Instructor
Daniel We
4.6 Average rating
233 Reviews
5,841 Students
21 Courses
Traveller

Daniel is a 28 year old entrepreneur ,data scientist and web analyst consultant. He holds a master degree as well as other major certificates from Google and others.

He is committed to support other people by offering them educational services to help them accomplishing their goals and becomming the best in their profession.

"In order to do the impossible you need to see the invisible"