Python For Machine Learning Tutorial For Beginners. Machine learning is the new buzz word all over the world across the industries. Everyone trying to learn machine learning models, classifiers, neural networks and other machine learning technologies. If you are willing to learn machine learning, but you have a doubt of how do you get started? Here Coding compiler gives answers to your questions. Let’s dive into this article, happy machine learning.
Python For Machine Learning
In this article we will talk about the important features of Python and the reasons it applies to machine learning, introducing some important machine learning packages, and other places where you can get more detailed resources.
Why Python for Machine Learning?
Python is well suited for machine learning. First, it is simple. If you are completely unfamiliar with Python but have some other programming experience (C or other programming languages), getting started is fast.
Second, Python’s community is strong. This makes Python documentation not only tractable but also easy to read. You can also find detailed answers to many questions on StackOverflow.
And again, the by-product of a strong community is the vast library of useful libraries (native to Python and third-party software) that basically solve all your problems (including machine learning).
Related Article: Advantages of Python
Is Python Slow?
Python is slow. It’s not the fastest language to implement, and having so many useful abstractions comes at a price.
But this is a problem that can be solved: Libraries can outsource heavy computations to other more efficient (but harder) languages such as C and C ++. Such as NumPy this numerical computing library is written in C, running fast. In practice, almost all libraries use NumPy to do the heavy lifting. If you see Numpy, you should think of it soon.
So you can make the program run faster with its low-level language to achieve the speed of operation compared. You do not need to worry about the speed of the program.
Machine Learning Tutorial For Beginners
Worth knowing python libraries for machine learning.
- Scikit-learn
- NLTK
- Theano
- TensorFlow
- Keras
- PyTorch
1) Scikit-learn
Have you just started to learn machine learning? If you need a library that covers all the features of feature engineering, model training, and model testing, scikit-learn is your best bet!
This great free software provides all the tools you need for machine learning and data mining. It is the current standard library for machine learning in Python. This library is recommended for use with any sophisticated machine learning algorithm.
This library supports both categorization and regression, implementing all of the classic algorithms (support vector machines, random forests, naive Bayes, etc.). The library design makes migrating algorithms so easy that experimenting with different algorithms is easy. These classic algorithms are highly usable and can be used in a large number of different situations.
But this is not the full functionality of Scikit-learn, it can also be used to do dimensionality reduction, clustering, whatever you can think of. Because it builds on Numpy and Scipy (all numerical calculations are done in C), it runs extremely fast.
These examples can tell you the function of this library, if you want to learn how to use it, you can read the tutorial.
2) NLTK
NLTK is not a machine learning library, but it is a library necessary for natural language processing (NLP). In addition to the features used for word processing, such as clustering, word segmentation, stemming, marking, parsing, etc., it also contains a large number of datasets and other lexical resources that can be used for model training.
3) Theano
Theano is widely used in industry and academia and is the originator of all deep learning architecture. Theano is Python, in conjunction with Numpy. You can use it to build neural networks with multidimensional arrays. Theano handles all the math and you do not need to know the underlying math formula implementation.
Theo already provided support for GPU computing as early as supporting the use of GPU for computing not as popular as it is today. This library is currently very mature and can support many different types of operations. This allows Theano to win when compared to other libraries.
Currently, the biggest problem with Theano is that APIs are not very useful and difficult to use for newbies. However, packages such as Keras , Blocks, and Lasagne that already have a solution to this problem can simplify the use of Theano.
TensorFlow Machine Learning
Google Brain Team created TensorFlow for internal use and turned it open in 2015. Designed to replace their existing DistBelief, a closed machine learning framework, it is said that the architecture is too dependent on Google’s overall architecture and not flexible enough to be very inconvenient when sharing code.
So there is TensorFlow. Google learned from previous mistakes. Many consider TensorFlow an improved version of Theano, which provides a more flexible and easy-to-use API. Can be used in scientific research and industry, while supporting the use of a large number of GPU model training. TensorFlow does not support Theano’s much more operations, but its computational visualization is better than Theano’s.
TensorFlow is currently very popular. If you just heard one of the names mentioned in this article today, it is most likely this. Every day, new posts to TensorFlow’s blog posts or academic articles are posted. This popularity provides a large number of users and tutorials, new people are very easy to use.
Keras
Keras is a library that provides higher-level neural network APIs that can be based on Theano or TensorFlow. It has the powerful features of both libraries while greatly simplifying ease of use. It puts the user experience in the forefront, providing simple APIs and useful error messages.
Keras’s design is module-based, which allows you to freely mix different models (neural layers, cost functions, etc.) and the model is very scalable because you only have to simply associate new modules with existing ones It can be up.
If you start with deep learning, take a look at examples and documentation and have a look at what you can do with it. If you want to learn to use it, can from this tutorial begins.
Two similar libraries are Lasagne and Blocks , but they only support Theano. If you’ve tried Keras but you do not like it you can try these other libraries, maybe they’re better for you.
PyTorch
There is also a famous deep learning architecture Torch , it is implemented with Lua. Facebook implemented Torch in Python, called PyTorch, and made it open source. With this library you can use the lower level library Torch uses, but you can use Python instead of Lua.
PyTorch is good at troubleshooting, because Theano and TensorFlow use symbolic computation and PyTorch does not. Using symbolic calculations means that an operation (x + y) will not be executed when a single line of code is interpreted, until then it must be compiled (interpreted as CUDA or C).
This makes it hard to troubleshoot problems with Theano and TensorFlow because it’s hard to relate the error to the current code. This has its advantages, but it is not easy to find the wrong one. If you want to start learning PyTorch, official documents for beginners will also contain difficult content.
The first step in machine learning?
Now, You know about so many machine learning packages, which one should I use? How can I compare them? Where do I start?
You can try our Ape Advice ™ platform for beginners and do not bother with the details. If you have absolutely no contact with machine learning, start with scikit-learn. You can see how labeling, training and testing work, and how a model is built.
If you want to try out in-depth learning, starting with Keras, this is the easiest framework to recognize. You can try it first to find the feeling. After you get a bit of experience, you can begin to think about what you need most: speed, different APIs, or whatever, and you’re better off later.
There are currently numerous articles comparing Theano, Torch and TensorFlow. No one can say which is the best. What you have to keep in mind is that all packages support a lot of things and are constantly improving, making it harder and harder to compare them to each other. Six months ago the standard may be outdated, a year ago’s assessment said the framework X does not have the Y function may not be effective.
If you want to know more about the concepts of machine learning, check out this Machine Learning Getting Started Guide.