Machine Learning - Fundamentals
Python Foundations & Classification Models using Data Sets
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Machine Learning - Fundamentals.
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A comprehensive list of all sections & lectures for this course can be found below.
What is Machine Learning? - 07:47
Basics of Machine Learning - 06:34
Installing Anaconda / Python Environment - 07:26
Downloading / Setting Up Atom and Plugins - 09:02
Variables in Python - 08:24
Functions, Conditionals, and Loops in Python - 09:50
Arrays and Tuples in Python - 13:52
Importing Modules in Python - 05:22
What is scikit-learn? Why use it? - 03:52
Installing scikit-learn and scipy with Anaconda - 03:28
Intro to the Iris Dataset - 03:28
Datasets: Features and Labels Explained - 07:39
Loading the Iris Dataset / Examining and Preparing Data - 09:27
Creating / Training a KNeighborsClassifier - 09:43
Testing Prediction Accuracy with Test Data - 12:08
Building Our Own KNeighborsClassifier - 18:00
Next Course: Analyze Visual Data with Machine Learning - 18:00
This course offers an insightful introduction into the world of Machine Learning. Here, students learn the foundational concepts, starting with installing a Python Environment, using Anaconda. Students then progress to learning Python basics including variables, arrays, and modules.
From there, students explore beginner level classification models using Scikit-learn and Spicy with Anaconda. This course also offers a great introduction to working with Data Sets and formulating predictions using KNeighborsClassifier.
What is Machine Learning?
"Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks." - Wikipedia
All course reviews are written by students who have completed the course or are currently enrolled.
Excelente
this course provides the understanding and power of Machine Learning.
I can not open the continue learning video.I have a problem.Any ideas
This was good for intro and beginner. Instructor has good knowledge aswell
like
Excellent course for beginners in ML. The course is well structured, explanations are clear, exercises are not so challenging but that's enough to apply the concepts exposed during the lectures.
The instructor was very knowledgeable and course was very well designed progressing from basics to some intermediate concepts. Overall it covers the foundations which is what I needed.
Course contains a lot of information and explored some libraries which are useful for data science and machine learning.
The course was really helpful and quite informative. Got to learn some amazing new concepts. Thanks
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