EITC/AI/MLP Machine Learning with Python

by admin / Tuesday, 02 February 2021 / Published in Uncategorized

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EITC/AI/MLP Machine Learning with Python is the European IT Certification programme on the fundamentals of programming machine learning with Python language.

The curriculum of the EITC/AI/MLP Machine Learning with Python focuses on theoretical and practical skills in machine learning programming organized within the following structure, encompassing comprehensive video didactic content as a reference for this EITC Certification.

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as the 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 unfeasible to develop conventional algorithms to perform the needed tasks. 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”.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, however not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the “signal” or “feedback” available to the learning system:

Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximize.

Other approaches have been developed which don’t fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, dimensionality reduction or meta learning.

As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning.

Python is an interpreted, high-level and general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects. Python is often described as a “batteries included” language due to its comprehensive standard library. Python is commonly used in artificial intelligence projects and machine learning projects with the help of libraries like TensorFlow, Keras, Pytorch and Scikit-learn.

Python is dynamically-typed (executing at runtime many common programming behaviours that static programming languages perform during compilation) and garbage-collected (with automatic memory management). It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming. It was created in the late 1980s, and first released in 1991, by Guido van Rossum as a successor to the ABC programming language. Python 2.0, released in 2000, introduced new features, such as list comprehensions, and a garbage collection system with reference counting, and was discontinued with version 2.7 in 2020. Python 3.0, released in 2008, was a major revision of the language that is not completely backward-compatible and much Python 2 code does not run unmodified on Python 3. With Python 2’s end-of-life (and pip having dropped support in 2021), only Python 3.6.x and later are supported, with older versions still supporting e.g. Windows 7 (and old installers not restricted to 64-bit Windows).

Python interpreters are supported for mainstream operating systems and available for a few more (and in the past supported many more). A global community of programmers develops and maintains CPython, a free and open-source reference implementation. A non-profit organization, the Python Software Foundation, manages and directs resources for Python and CPython development.

As of January 2021, Python ranks third in TIOBE’s index of most popular programming languages, behind C and Java, having previously gained second place and their award for the most popularity gain for 2020. It was selected Programming Language of the Year in 2007, 2010, and 2018.

An empirical study found that scripting languages, such as Python, are more productive than conventional languages, such as C and Java, for programming problems involving string manipulation and search in a dictionary, and determined that memory consumption was often “better than Java and not much worse than C or C++”. Large organizations that use Python include i.a. Wikipedia, Google, Yahoo!, CERN, NASA, Facebook, Amazon, Instagram.

Beyond its artificial intelligence applications, Python, as a scripting language with modular architecture, simple syntax and rich text processing tools, is often used for natural language processing.

To acquaint yourself in-detail with the certification curriculum you can expand and analyze the table below.

The EITC/AI/MLP Machine Learning with Python Certification Curriculum references open-access didactic materials in a video form by Harrison Kinsley. Learning process is divided into a step-by-step structure (programmes -> lessons -> topics) covering relevant curriculum parts. Unlimited consultancy with domain experts are also provided.
For details on the Certification procedure check How it Works.

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". The EITC/AI/MLPP Machine Learning Programming with Python programme aims in introducing fundamentals of machine learning (including basic understanding of the theory) focusing on programming with Python. Except of the theory it covers applications along with theoretical and practical aspects of supervised, unsupervised, and deep learning machine learning algorithms. The programme covers linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. It includes basic notions of the algorithimcs involved and the logic behind. It also covers discussion of the applications of the algorithms in programming using examplary real data sets along with modules (e.g. Scikit-Learn). The programme will also cover details of each of the algorithms by implementing these algorithms in code, including the involved mathematics with insights into how exactly the algorithms work, how they can be modified, and what are their properties, including advantages and disadvantages. The algorithmics involved in machine learning are rather simple (as conditioned by their scaling necessity for large data sets), as is the mathematics which they are based on (linear algebra).