- Practical Machine Learning Tutorial with Python Intro p.1

Practical Machine Learning Tutorial with Python Intro p.1

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 ...
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.

https://pythonprogramming.net/machine-learning-tutorial-python-introduction/

https://www.facebook.com/pythonprogra...
https://plus.google.com/+sentdex

#machine learning #python #tutorial #artificial intelligence #scikit-learn #theano #tensorflow #supervised machine learning #unsupervised machine learning #linear regression #classification #clustering #k nearest neighbors #support vector machine #deep learning

sentdex

🎉 1,100,000 人達成!  📈 予測:200万人まであと3690日(2033年1月13日) 

Timetable

動画タイムテーブル

動画数:72件

at  what's the difference between "prev_obs.reshape(-1,4,1)[0]" and "prev_obs.reshape(-1,4)" ?if there is none, why do the first one? is that a habit from something else? - Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4

at what's the difference between "prev_obs.reshape(-1,4,1)[0]" and "prev_obs.reshape(-1,4)" ?if there is none, why do the first one? is that a habit from something else?

Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4
2017年03月13日
00:03:40 - 00:16:16
at  Lord Sentdex said that we take the 0th element of the list because we are only predicting on one frame.Anyone knows how to make predictions based on multiple frames ? - Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4

at Lord Sentdex said that we take the 0th element of the list because we are only predicting on one frame.Anyone knows how to make predictions based on multiple frames ?

Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4
2017年03月13日
00:04:00 - 00:16:16
Hey Sentdex, what do you mean by 'headless' at ? What type of example is headless and what type isn't? - Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4

Hey Sentdex, what do you mean by 'headless' at ? What type of example is headless and what type isn't?

Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4
2017年03月13日
00:04:50 - 00:16:16
lol, @ "Harrison, always messing with future Harrison". - Training Model - Training a neural network to play a game with TensorFlow and Open AI p.3

lol, @ "Harrison, always messing with future Harrison".

Training Model - Training a neural network to play a game with TensorFlow and Open AI p.3
2017年03月13日
00:13:30 - 00:14:38
someone gets a score of over 9000! - Intro - Training a neural network to play a game with TensorFlow and Open AI

someone gets a score of over 9000!

Intro - Training a neural network to play a game with TensorFlow and Open AI
2017年03月13日
00:02:20 - 00:12:32
The links you mentioned at  are missing - Intro - Training a neural network to play a game with TensorFlow and Open AI

The links you mentioned at are missing

Intro - Training a neural network to play a game with TensorFlow and Open AI
2017年03月13日
00:05:00 - 00:12:32
/x pass dtype wherever u r making np.array()2. if u r getting like target_1/y use tf.compat.v1.reset_default_graph() just after the wherever u have imported tensorflow.Some points which I faced and their solution:1. if u r working on newer numpy, always pass dtype = object in the parameter of np.array()2. I am using tensorflow 2.xx    tflearn 0.05(something like this).3. If u have version compatibility issues with tf2.0.0   I made a virtual env using conda then used pip to install latest of everything. - Training - Using Convolutional Neural Network to Identify Dogs vs Cats p. 3

/x pass dtype wherever u r making np.array()2. if u r getting like target_1/y use tf.compat.v1.reset_default_graph() just after the wherever u have imported tensorflow.Some points which I faced and their solution:1. if u r working on newer numpy, always pass dtype = object in the parameter of np.array()2. I am using tensorflow 2.xx tflearn 0.05(something like this).3. If u have version compatibility issues with tf2.0.0 I made a virtual env using conda then used pip to install latest of everything.

Training - Using Convolutional Neural Network to Identify Dogs vs Cats p. 3
2017年02月23日
00:00:00 - 00:19:02
[<?, ?it/s] ".I don't know how to fix it can you please help me to solve this error and thank you in advance - Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2

[<?, ?it/s] ".I don't know how to fix it can you please help me to solve this error and thank you in advance

Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2
2017年02月23日
00:00:00 - 00:09:05
[<?, ?it/s] and there seems to be nothing happening. Could you please help me on this. - Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2

[<?, ?it/s] and there seems to be nothing happening. Could you please help me on this.

Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2
2017年02月23日
00:00:00 - 00:09:05
[<00:00, 804.72it/s] - Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2

[<00:00, 804.72it/s]

Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2
2017年02月23日
00:00:31 - 00:09:05
tqdm is available on kaggle kernel. ( in video) - Intro and preprocessing - Using Convolutional Neural Network to Identify Dogs vs Cats p. 1

tqdm is available on kaggle kernel. ( in video)

Intro and preprocessing - Using Convolutional Neural Network to Identify Dogs vs Cats p. 1
2017年02月23日
00:08:28 - 00:12:51