Data Science Learnings

How many times have you tried to predict the sales, the weather or the stocks with good accuracy? Often times we may find the good Neural model yet we fail to tune it's hyper-parameters. Here's where deep learning model, PROPHET will be useful much for beginners in developing predictive models. Let's begin!

In supervised learning, we train the machine using data which is well labelled.   It means some data is already tagged with the correct answer. Types of Supervised Machine Learning Algorithms: Regression (Linear and Multi-linear) Logistic Regression Classification Naïve Bayes Classifiers Decision Trees Support Vector Machine (SVM) Challenges in Supervised machine learning: Irrelevant input feature present training data could give inaccurate or wrong results. Data preparation and pre-processing is always a challenge. Accuracy suffers when impossible, unlikely, and incomplete values have been inputted as training data. If the concerned expert is not available, then the other approach is "brute-force." It means you need to think that the right features to train the machine on. It could be inaccurate. Advantages of Supervised Learning: Supervised learning allows you to collect data or produce a data output from the previous experience. Helps you to optimize performance criteria u

In the previous blog, we had discussed brief about What is Machine Learning? In this blog, we are going to learn about the types of ML.  ML is broadly classified into four types: Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning 1. Supervised Learning Supervised learning is where there are input variables, say X and there are corresponding output variables, say Y. We use a particular algorithm to map a function from input(X) to output(Y). Mathematically, Y=f(X). Majority of the ML models use this type of learning to feed itself and learn. The goal of supervised learning is to approximate the said function so well that whenever we enter any new input, it's output is accurately predicted. Here, we can say that there is a teacher who guides the model if it generates incorrect results and hence, the machine will keep on learning until it performs to desired results. Supervised Learning can be further classified into: Classification : Here, the ou

Arthur Samuel, firstly coined the term "Machine Learning". He defined the term as, "Field of study that gives computers the capability to learn without being explicitly programmed." Explaining in layman terms, Machine learning means improving the process of learning for computers which is based on it's experiences to do a certain task without further guidance through programs. In other words, we can say that machine learns through initial program and feeds itself the data which obtained from the experiences while executing a particular task. Let's take an example to understand this. A father and a baby went to a park to make the baby learn how to walk. Initially, the father hold the hands of his baby so that the baby can walk without tripping. As the baby can now stand on it's own legs, the father did not hold hands of the baby, thus the baby kept going on and  tripped as stone hit the toes. The baby stood up and learned not to walk over stones. The next