Introductory Machine Learning Algorithms in Python with scikit-learn
Instructor
Hannah Davis
Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.
In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.
We’ll implement these in Python using scikit-learn using scikit-learn’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms.
We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification).
Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the print statement API.
For additional information on installation, vocabulary, and common errors visit the README.md to the course code attached to each lesson.
Learner Reviews
- Marcus Holmgren5 years agoWhat did you like about this course?
Nice overview of sklearn library and it's features.
- Adam Mac6 years agoWhat was your strongest take away from this course?
The title of this course (to me) seemed to indicate that it was an introduction to the algorithms more generally (which would, presumably, be accompanied by practical code examples). However, this series seems to be more about demonstrating the specific way that SciKit Learn implements the various concepts of Machine Learning.
As someone with no ML experience, I was a bit disappointed to discover that this wasn't for me. With that said, I would certainly still recommend this course to anyone looking for instruction on applying their ML knowledge to SKL specifically.
- Michel Mattos6 years agoWhat would make this course a 7 for you?
More links to additional info about the models showed (tho the scikit-learn documentation are good, some of them looks too crypt with lots of math equations)
- Christopher7 years agoWhat did you like about this course and how will it help you?
I really Hannah Davis. Very clear voice, no typos. Explains things well, if a bit too fast. Since I'm into data science, I'd watch more videos.
- daedalus317 years ago
As a starting point to explore machine learning further.
- Michail Papargyriou7 years ago
I want a video with Sentiment Analysis. Thanks for videos!
Course Content
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