Data Analytics Using Python

Machine Learning Terminologies and Processes. Introduction:     Hi and welcome all to my blog in which we are going to discuss about machine learning terminology and processes. We'll see end to end machine learning modelling process, The process begins with business problems and then lead to machine learning problem we'll discuss how that happens and how data goes through preprocessing and modelling process followed by predictioning the output.      Before we proceed with machine learning process lets have a look at various commonly used machine learning terminology. coming to the basic terminology i.e. Training, Model and Prediction. Training is the process where we train our model based on the historical data the 'Model' then analyzes various patterns in the data and be self sufficient to make future predictions on the unseen data.  Typically to train the model we split our data into 2 parts which is Training set and other is Testing set. The training dataset is shown

 Gradient Descent Optimizing Technique for Machine Learning  Have you ever applied any Machine Learning algorithm on a data and found that the output is not as expected and have thought over that even applying the correct algorithm with correct syntax and logic. Here is what can help you to come over that and get the expected output i.e. optimization . There are various optimizing techniques of which one is Gradient Descent and we'll focus on that in this blog, So lets gets started. Optimization: an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible Introduction to Gradient Descent: Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent. These algorithms, however, are oft