Machine Learning with Python by Sentdex
Comprehensive Machine Learning series covering everything from linear regression to neural networks provided by a famous YouTube instructor, Sentdex. This tutorial features 72 videos, and it's ideal for learners that have a basic understanding of Python.
Created by: Harrison Kinsley
Produced in 2016
What you will learn
- Use Titanic Dataset to learn K-Means and Mean Shift clustering algorithms
- Predict stock prices using linear regression
- Train a neural network to play the CartPole game
- Build your own algorithms in Python from scratch
- Get familiar with Kaggle Data Science Bowl competition
- Use Scikit-Learn to apply algorithms
- K-Nearest Neighbors and Support Vector Machine classification algorithms
- Recurrent and Convolutional Neural Networks with TensorFlow
- Theory and application of important Machine Learning concepts
- Much, Much more!
Quality Score
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Overall Score : 92 / 100
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Course Description
machine learning Awards Best YouTube Tutorial
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.
This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math.
If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way.Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it.
In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.
This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math.
If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way.Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it.
Pros
Cons
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- In-depth tutorial covering many major topics of Machine Learning.
- Great for beginners as well as intermediate level learners.
- Interesting and knowledgeable instructor with practical approach to learning.
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- Requires foundational knowledge in data science and Python.
Instructor Details
- 4.6 Rating
15 Reviews
Harrison Kinsley
Harrison Kinsley is a husband, runner, friend of all dogs, programmer, teacher, and entrepreneur. He likes to learn and build with technology. He is a founder of multiple businesses, all of which leverage the Python programming language. From using Flask web development on all of his business sites, to Scikit-Learn and Pandas for machine learning and data analysis with Ensmo.com, to the Natural Language Toolkit for natural language processing with Sentdex.com, to teaching a massive variety of Python programming topics on PythonProgramming.net, Python and programming is a major part of his life and work.
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- Video Duration:
19h - Price: Free 0 Track Price
- Provider:
YouTube - Year:
2016 - Level:
Beginner - Language:
English
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