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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