Machine Learning: A Guide to PyTorch, TensorFlow, and Scikit-Learn: Mastering Machine Learning With Python PDF 2024..
Introduction to Machine Learning with Python - Theory:
we propose you to discover the basics of machine learning and to introduce you to it with the Python language.
This first part is non-technical and presents the concepts of machine learning, the different types of learning and their main algorithms. He finally places Python in this universe by presenting the many libraries at your disposal to address this discipline.
Introduction: This article is the first part of a course on machine learning with Python. In this first part, we try to offer you a vision of the different areas of artificial intelligence and how Python stands out.
It continues with a detailed presentation of machine learning, which today is certainly the most active branch of this discipline to the point of being on the lips of all large companies. We will try to lay the foundations to help you grasp the global view, the famous «big picture», which will help you to choose your algorithms and libraries.
We will present: How Python is Willing to Do AI A Quick History of AI
Machine learning, its domains and main algorithms:
What are the qualities of a language for AI and why use Python? Python, as a single language, is not specifically more suited than other languages to make AI. Wikipedia, in its English version, presents a page describing the languages most used for AI. We find Python, but not for his internal qualities. For AI it is especially useful that the language allows: Recursion: Today almost all languages allow it, only non-procedural languages, such as the old BASIC of TO8 not offering the notion of function are devoid of this ability. However, tail call type optimizations are not available in Python. Object-oriented programming Again, most of the modern languages commonly used have them. Python offers all the required mechanics: inheritance, overload, virtual methods; even if it is less rigorous than others (no private/public/protected visibility).
The functional paradigm:
Python allows this programming paradigm even if it is less "natural" than in other languages like Haskell, Lisp or Erlang. Logical programming features, such as the predicate logic used by PROLOG. Python offers nothing like this in native. Clearly, Python is not really above other artificial intelligence languages. But it lends itself well and its concise and easy syntax allows to progress there certainly more easily than in other languages. He also has some specialized AI bookstores that allow him to learn this discipline. In addition, when it comes to machine learning, the most difficult issue is often having good data sets - and this sometimes goes well beyond the need to know how to correctly implement or use a machine learning algorithm. Python is particularly well equipped with libraries like Numpy or Pandas, for example. Finally, in the field of machine learning, Python stands out in particular by offering a plethora of high-quality libraries, covering all types of learning available on the market; All accompanied by a large and dynamic community. For example, the Scikit-Learn project, which is one of the most renowned for this discipline, has nearly 800 contributors. This is huge! Imagine a team of 800 developers. Few companies and IT projects can compare to this number. But it is necessary to relativize, they are far from all working there full time. Definition: *Adapted from Wikipedia* Machine learning can be seen as the set of techniques that allow a machine to learn to perform a task without having to explicitly program it for it. Arthur Samuel. Definition taken from an article by Antoine Gaudelas: Machine learning or statistical learning is an area of artificial intelligence that concerns the design, analysis, development and implementation of methods allowing a computer to evolve through a systematic process, and thus to perform difficult or problematic tasks to be performed by more traditional algorithmic means. In other words, machine learning is one of the areas of artificial intelligence designed to allow a computer to learn knowledge and then apply it to perform tasks that until then we had sub-contracted to our reasoning. The type of tasks dealt with generally consists of data classification problems: Here are 50 photos of my daughter, here are now all the photos of my album, find the ones where my daughter is Here are 10,000 people with their characteristics (age, location, size, occupation...), bring them together in 12 coherent groups sharing the same common points Here are the flight parameters of the aircraft in my fleet with the failures that occur on each flight, when they occur. Now here are the last flights made by this plane, tell me when the next failure will happen and on what element it will happen. Here are the books written by Victor Hugo, here is a short story of which we are looking for the author, is it from him? Here are scientific measurements made on a sample of data, they seem to respond to a Mathematical law, try to approximate it by Mathematical functions and then predict the values of the next data. The possibilities are very great and there is machine learning in many areas of real life such as: Computer vision Driving without a driver The prediction of breakdowns Identification of undecided individuals likely to be convinced by a candidate in the US presidential election Power consumption optimization at Google Monitoring illegal fishing or deforestation Linguistic translation
Choose your learning type and algorithm:
It is necessary to combine several ingredients that need to be skillfully mixed to achieve an AI: The quality of the data Computing power (the hardware) The algorithms And talent When you ask a machine learning expert what algorithm is best for a particular problem, they usually say: it depends, you have to try several and see which one works best on this case And indeed it depends: The quality of the data you have for learning and classification Parameters you use with your data Quantity of source data and to be classified Of the required execution time Parameters available to influence algorithm behavior ... In general, you need to try several to get an idea. Sometimes you will really use several, depending on the desired precision, if the one that is generally the best gives an unsatisfactory result on a specific dataset, another will perhaps do better on this case. Here are already some links, for lack of better at the moment, to help you choose your algorithms according to your classification needs: Choose your algorithm: Fortunately there are techniques to measure the quality of an algorithm on a dataset. We will introduce you to some tools to achieve this. We will see then that on some areas, some algorithms seem automatically intended to respond with the most relevance. Language Python: Create innovative applications with Python: Makina Corpus places Python at the heart of its technical strategy since its beginnings and recruits among the best talents in the sector. Python is a pragmatic open source language, designed to quickly produce robust, readable and maintainable code through indentation. It is designed to optimize the productivity of developers by offering high-level tools. Python is a software language with a translated feature that contains many different models and platforms. This language promotes structured, functional and object-oriented imperative programming. It features strong dynamic typing, automatic memory management by crumb pickers and an exception management system. Like the PHP language, the code is interpreted at runtime without a build step. The latter is nevertheless compiled in an intermediate bytecode format to optimize its performance. Through its many contexts of use, it federates very different profiles of computer scientists: Engineers, Web application developers, Desktop application developers, Researchers and scientists, Data scientists, System administrators and devops, students, etc. Machine Learning and Python: Python has established itself/established itself in the scientific and industrial world. The field of machine learning has not been left out, on the contrary... The tremendous possibilities of language calculation have made it possible to penetrate this sector and multiple bookshops have been born. Annoy, extremely fast library implementing the search for nearest neighbors: Caffe, Deep learning framework Chainer, Intuitive framework for neural networks neon, Deep Learning framework extremely powerful NuPIC, AI platform implementing HTM learning algorithms Shogun, Large Scale Machine Learning Toolbox TensorFlow, Neural network with high-level API Torch, High-performance learning algorithm framework with Python binding. Theanets, deep learning ... The most amazing is that they are all generally of high quality and used in professional environments. However, Scikit-Learn is probably the most popular library available for this language. It has a large number of features specialized in data analysis and data mining that make it a tool of choice for researchers and developers. To go further The Wikipedia site contains very good references on artificial intelligence O'Reilly has a great AI blog and has some very interesting free books The New Artificial Intelligence Market Intelligence Artifical Now What Is Artificial Intelligence The OpenAI Association founded by Elon Musk proposes to develop an open Artificial Intelligence working for the good of humanity It provides many resources including to test your machine learning algorithms.
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