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  Introduction to Transfer Learning We, humans, are very perfect in applying the transfer of knowledge between tasks. This means that whenever we encounter a new problem or a task, we recognize it and apply our relevant knowledge from our previous learning experiences. This makes our work easy and fast to finish. For instance, if you know how to ride a bicycle and if you are asked to ride a motorbike which you have never done before. In such a case, our experience with a bicycle will come into play and handle tasks like balancing the bike, steering, etc. This will make things easier compared to a complete beginner. Such leanings are very useful in real life as it makes us more perfect and allows us to earn more experience. Following the same approach, a term was introduced  Transfer Learning  in the field of machine learning. This approach involves the use of knowledge that was learned in some task, and apply it to solve the problem in the related target task. While most machine learni

  Random Forest Regression in Python Every decision tree has high variance, but when we combine all of them together in parallel then the resultant variance is low as each decision tree gets perfectly trained on that particular sample data and hence the output doesn’t depend on one decision tree but multiple decision trees. In the case of a classification problem, the final output is taken by using the majority voting classifier. In the case of a regression problem, the final output is the mean of all the outputs. This part is Aggregation. A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as  bagging . The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees. Random Forest has multiple decision trees as base learning models. We randomly perfo

  Decision Tree Decision Tree: The decision  tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.    A decision tree for the concept plays tennis.   Construction of Decision Tree :   A tree can be  “learned”  by splitting the source set into subsets based on an attribute value test. This process is repeated on each derived subset in a recursive manner called  recursive partitioning . The recursion is completed when the subset at a node all has the same value of the target variable, or when splitting no longer adds value to the predictions. The construction of a decision tree classifier does not require any domain knowledge or parameter setting, and therefore is appropriate for exploratory knowledge discovery. Decision trees can handle high-dimensional d

  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 that used

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 Gmail or Hotmail does a lot of hard work for us in classifying th

  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. Speech Re

   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, experiences, learning, and insigh

  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 restructure the data into so

  What is Intelligence? The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use the natural language fluently, classify, generalize, and adapt to new situations. Basic Concept of Artificial Intelligence (AI) According to the father of Artificial Intelligence , John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how the human brain thinks and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis for developing intelligent software and systems. While exploiting the power of the computer systems, the curiosity of hum

  What is Artificial Intelligence? In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines. Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, painting, etc. AI is one of the fascinating and universal fields of Computer science that has a great scope in the future. AI holds a tendency to cause a machine to work as a human. Artificial Intelligence exists when a machine can have human-based skills such as learning, reasoning, and solving problems With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorith

  This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs)  1. When talking to a speech recognition program, the program divides each second of your speech into 100 separate __________ a) Codes b) Phonemes c) Samples d) Words Answer: c 2. Which term is used for describing the judgmental or commonsense part of problem solving? a) Heuristic b) Critical c) Value based d) Analytical Answer: a 3. Which stage of the manufacturing process has been described as “the mapping of function onto form”? a) Design b) Distribution c) Project management d) Field service Answer: a 4. Which kind of planning consists of successive representations of different levels of a plan? a) hierarchical planning b) non-hierarchical planning c) project planning d) all of the mentioned Answer: a 5. What was originally called the “imitation game” by its creator? a) The Turing Test b) LISP c) The Logic Theorist d) Cybernetics Answer: a 6. Decision support programs are designed to help managers