"Machine learning will automate the functions that most people thought were performed only by humans."
Dev Water (1)
"Have you ever wondered: How can Google predict what you're looking for? How does Amazon guess which products you want to buy? Or how does Netflix succeed in suggesting offers you might like?" A kind of artificial intelligence that learns and develops itself when it receives new data. "This is one of dozens of answers posted on the Quora platform to simplify the concept of machine learning in response to questions about its nature." Expertsystem " "Machine learning" provides systems with the ability to learn automatically and evolve through experience and without having to fully program it Focuses on the development of computer programs to be able to access the data and use it to learn from it, "ie to learn what it had to do and make decisions automatically without the need for human intervention.
The application of this technology is increasingly important in all sectors and is currently being used in the development of the financial services sector to prevent fraud attempts, to help investors determine the best times to invest, in the health sector to identify trends and risk signals, and in the energy sector to find new resources and to achieve cost-effective use of existing resources. This requires more professionals able to manage and develop its operations, which has led to an increase in the number of vacancies for them. Traditional and electronic educational programs are designed to provide those interested in the field with the skills and knowledge Privatization, what are the opportunities available to study, and the nature of vacancies in this area?
Machine learning in universities
Academic institutions did not devote programs to study machine learning at the undergraduate level in a specialized and individual basis on the technological bases, but included them in educational courses within the plans of programs of this stage and specifically computer science programs, and this is also followed by academic institutions at the graduate level, but a number of others began in Develop master programs focusing on machine learning.
Master in Machine Learning
KTH Royal Institute of Technology, Sweden, one of the best academic education institutions in the world, offers through its College of Computer Science and Electrical Engineering a program for the study of machine learning and an MSc in Machine Learning It aims to teach students the mathematical and statistical bases and methodologies underlying machine learning, and enhances the students' practical skills necessary to work in this field.
The two-year full-time program is offered in English, and its curriculum includes a range of courses covering applied mathematics, statistics, and how to use machine learning to meet challenges in computer vision, information retrieval, language processing, robotics and computational biology. Students in an industrial applied project or academic research work inside or outside Sweden.
Online machine learning
The e-learning resources available for machine learning vary to include, among other materials, individual e-courses offered in academic or specialized programs, for example an e-program for the Master of Science in Machine Learning Coursera also offers a range of specialized programs in the same field, including:
Mathematics in Machine Learning
Learning machine learning requires the skills of a person, the most important of which is mathematics and how to use it in computer science, so learning mathematics is an introduction to the understanding of machine learning, and in this context are offered many e-courses, including a specialized program offered by one of the top ten universities in the world "Imperial College London" (Imperial College London) via the Coursera platform, with a monthly fee of $ 49.
The Mathematics for Machine Learning program requires two months to complete its teaching requirements at a rate of 12 hours per week, and is suitable for people who want to learn the applications of mathematics in data science and machine learning, and the plan includes three courses, the first "mathematics in machine learning : Mathematics for Machine Learning: Linear Algebra, focuses on linear algebra and its relationship to vectors and matrices, how to deal with them, and how to use them in doing entertaining things within databases such as face image management. Line Automated learning.
The second course introduces Mathematics for Machine Learning Multivariate Calculus to its topic and its relevance to building machine learning techniques and tools, and covers related aspects such as how to build tools that facilitate and accelerate the use of calculus. Machine Learning: Mathematics for Machine Learning (PCA) introduces participants to Principal Component Analysis and covers some statistical bases used in dealing with databases such as "arithmetic mean" and "variance", This course is suitable for those with good knowledge Linear algebra and basic calculus multi variables in programming using the language of "Bataon" (Pathyon).
Intermediate level machine learning:
The University of Washington, the United States, one of the top 100 universities in the world, also offers on Coursera a specialized machine learning program suitable for middle-level students in the field, and has so far enrolled more than 69,000 participants, including Data Scientists, Risk Managers, Data Analysts, Machine Learning Engineers and Data Engineers are charged US $ 49 per month over the 8 months required to complete the program at an average of 7 hours of instruction per week.
In addition to the application of machine learning in the prediction of house prices, it has other applications such as predicting health outcomes in medicine, stock prices in finance and energy use in high-performance computing.
The learning materials of the program are presented in English in addition to the translation of photocopying lessons in Arabic. The Machine Learning program includes four courses, starting with Machine Learning Foundations: A Case Study Approach. ), Which provides participants with hands-on machine learning training using a set of hands-on case studies to teach them how to estimate house prices based on their advantages, analyze feelings about them through user reviews, and retrieve documents reflecting home interest, recommendation tools, and image search inputs.
In addition to the application of machine learning in predicting house prices, it has other applications such as predicting health outcomes in medicine, stock prices in finance and energy use in high performance computing. In the second course "Machine Learning: Regression", participants are trained to use regression models. Linearity in the performance of prediction tasks, analysis of the impact of data on predictions and selected models, description of the inputs and outputs of the regression model, analysis of the performance of models, and the use of the language "Patheon" in it.
Participants in Machine Learning: Classification move to learn how to build workbooks to raise the performance of various tasks using a range of practical tools, such as logistic regression and decision tree, and how to analyze financial statements to predict loan default rates, and apply them all. Using the language "Pathion" or one of the languages chosen by the student, while the last course "Machine Learning: Clustering & Retrieval", participants are trained in, inter alia, building a document recovery system, collecting documents using the "taxonomic algorithm" , Dealing with growth Model Gaussian model using the expectation maximization algorithm.
Advanced Machine Learning
Higher School of Economics offers the same platform as the Advanced Machine Learning program, with a monthly fee of US $ 49, for data engineers, data scientists, machine engineers, and data analysts. The biostatistics specialist focuses on one aspect of machine learning: deep learning, which paves the way for him and for enhanced learning, natural languages, computer vision, and Bayesian methods, and includes seven courses.
The first course, Introduction to Deep Learning, is designed to provide participants with an understanding of the fundamentals of modern neural networks and their applications in computational vision and natural languages. They have a background on the use of the Pathion language, linear algebra and probability, and in the second course, “How to Win a Data Science Competition: Learn from the Best Kagglers” Participants gain an understanding of predictive modeling. And skills to solve their problems in the real world, Witt They are trained in a range of engineering techniques, machine learning methods, data analysis and translation, and more.
The Bayesian Methods for Machine Learning course provides an understanding of the basics of Bayesian methods and their applications in deep learning and how to generate new images using them. Participants will learn how to generate new pharmaceuticals for the treatment of serious diseases using these methods. The Practical Reinforcement Learning course focuses on reinforced learning, its foundations, the use of deep neural networks in the performance of its tasks, and the latest algorithms in its field, followed by the course "Deep Learning in Computer Vision", which aims to provide Participants understand computer vision Of starting from the basics and even the latest models of deep learning, as well as to address the definition of images and videos, and will be required to implement practical projects for the application of how to build a system to identify faces and manipulate them to understand this technique.
The sixth course, Natural Language Processing, covers the range of tasks used in natural language processing, such as emotion analysis and summarization, enables participants to learn about natural language processing tasks in daily work, and includes an application project in which the participant builds his own system. Chat-Pot, and performs other required tasks. The latest course deals with “Addressing Large Hadron Collider Challenges by Machine Learning”, “Large Hadron Collider”. Currently, participants are presented with the principles of physics Experimental and machine learning underlying the collider work in the data flow.