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  How Can You Transfer your career to Data Analytics? No matter what field you come from and what work experience you have, you can steer your career into Data Analytics . If you are still wondering what your next steps should be, here is a comprehensive guide that you can refer to. Based on your background, what skills you need to pick up to transition to a data-driven profession, we list down what you can expect! Are you a Fresher? The good news is that a lot of companies tend to hire fresh college graduates and train them in-house. They need an unbiased, fresh pair of eyes to look at their business problems. As a fresher, you don’t have any baggage, and the biggest advantage is that, as a professional, you can be molded in any way.  Make yourself conceptually sound in statistics, learn relevant tools and languages to demonstrate your skillset.   If you have technology experience , for instance in software engineering, or if you are an IT professional, then you certainly have a bi

  Machine Learning with Python: Zero to GBMs Machine Learning with Python: Zero to GBMs A beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python and its ecosystem of ML libraries: scikit-learn, XGBoost, and LightGBM. Earn a verified certificate of accomplishment by completing practical assignments and building a real-world course project. Watch hands-on coding-focused video tutorials Practice coding with cloud Jupyter notebooks Build an end-to-end real-world course project Earn a verified certificate of accomplishment Interact with a global community of learners You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world dataset. Prerequisite:  Data Analysis with Python: Zero to Pandas . Lesson 1 - Linear Regression with Scikit Learn Preparing data for machine learning Linear regression with multiple features Generating predictions and evaluating models Lesson 2 - L

  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