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  Best Python libraries for Machine Learning Machine Learning, as the name suggests, is the science of programming a computer by which they are able to learn from different kinds of data. A more general definition given by Arthur Samuel is – “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” They are typically used to solve various types of life problems.  In the older days, people used to perform Machine Learning tasks by manually coding all the algorithms and mathematical and statistical formulas. This made the processing time-consuming, tedious, and inefficient. But in the modern days, it is become very much easy and efficient compared to the olden days by various Python libraries, frameworks, and modules. Today, Python is one of the most popular programming languages for this task and it has replaced many languages in the industry, one of the reasons is its vast collection of libraries. Python libraries t...

Machine Learning – Applications   Introduction Machine learning is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect. We probably use a learning algorithm dozens of times without even knowing it. Applications of Machine Learning include: Web Search Engine:  One of the reasons why search engines like google, bing, etc work so well is because the system has learned how to rank pages through a complex learning algorithm. Photo tagging Applications:  Be it Facebook or any other photo tagging application, the ability to tag friends makes it even more happening. It is all possible because of a face recognition algorithm that runs behind the application. Spam Detector:  Our mail agent like G...

  Machine Learning: What is it really? Well, Machine Learning is a subfield of Artificial Intelligence that evolved from Pattern Recognition and Computational Learning theory. Arthur Lee Samuel defines Machine Learning as a Field of study that gives computers the ability to learn without being explicitly programmed. So, basically, the field of Computer Science and Artificial intelligence that “learns” from data without human intervention. But this view has a flaw. As a result of this perception, whenever the word Machine Learning is thrown around, people usually think of “A.I.” and “Neural Networks that can mimic Human brains ( as of now, that is not possible)”, Self Driving Cars and what not. But Machine Learning is far beyond that. Below we uncover some expected and some generally not expected facets of Modern Computing where Machine Learning is in action. Machine Learning: The Expected We’ll start with some places where you might expect Machine Learning to play a part. ...

   Introduction to Data in Machine Learning DATA:  It can be any unprocessed fact, value, text, sound, or picture that is not being interpreted and analyzed. Data is the most important part of all Data Analytics , Machine Learning, Artificial Intelligence. Without data, we can’t train any model and all modern research and automation will go in vain. Big Enterprises are spending lots of money just to gather as much certain data as possible. Example:  Why did Facebook acquire WhatsApp by paying a huge price of $19 billion? The answer is very simple and logical – it is to have access to the users’ information that Facebook may not have but WhatsApp will have. This information of their users is of paramount importance to Facebook as it will facilitate the task of improvement in their services. INFORMATION:   Data that has been interpreted and manipulated and has now some meaningful inference for the users. KNOWLEDGE:  Combination of inferred information, expe...

  Classification of Machine Learning Machine learning implementations are classified into three major categories, depending on the nature of the learning “signal” or “response” available to a learning system which is as follows:-    Supervised learning:  When an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples that comes under the category of Supervised learning. This approach is indeed similar to human learning under the supervision of a teacher. The teacher provides good examples for the student to memorize, and the student then derives general rules from these specific examples. Unsupervised learning:  Whereas when an algorithm learns from plain examples without any associated response, leaving to the algorithm to determine the data patterns on its own. This type of algorithm tends to rest...

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