 ## A Step by Step Guide to Logistic Regression Model Building using Python | Machine learning

In the field of Machine Learning, logistic regression is still the top choice for classification problems. It is simple yet efficient algorithm which produces accurate models in most of the cases. In its basic form, it uses the logistic function to calculate the probability score which helps to classify the binary dependent variable to its respective class. Logistic regression is the transformed form of the linear regression. In this post I have explained the end to end step involved in the classification machine learning problems using the logistic regression and also performed the detailed analysis of the model output with various performance parameters. ## Artificial Intelligence | hype vs reality | Fundamentals of Artificial neural network | ANN

Artificial neural networks (ANNs), usually called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains. There is lot of hype these days regarding the Artificial Intelligence and its technologies.

In this article, we will talk about the Hype vs Reality on AI technologies and also will explain about the various terminology associated with Artificial Intelligence (AI), Transitioning from Machine Learning to Deep Learning, Basic building blocks for the study of AI and Artificial Neural Network (ANN). ## Beginners Guide to Text Classification | Machine Learning | NLP | part 8

In this post, we will develop a classification model where we’ll try to classify the movie reviews on positive and negative classes. I have used different machine learning algorithm to train the model and compared the accuracy of those models at the end. you can keep this post as a template to use various machine learning algorithms in python for text classification.

At the end we will validate the model by passing a random review to the trained model and understand the output class predicted by the model. You will learn how to create and use the pipeline for numerical feature extraction and model training together as a one function. ## Word2Vec and Semantic Similarity using spacy | NLP spacy Series | Part 7

Word vectors – also called word embeddings – are mathematical descriptions of individual words such that words that appear frequently together in the language will have similar values. In this way we can mathematically derive context. As mentioned above, the word vector for “lion” will be closer in value to “cat” than to “dandelion”. ## Numerical Feature Extraction from Text | NLP series | Part 6

Machine Learning algorithms don’t understand the textual data rather it understand only numerical data. So the problem is how to convert the textual data to the numerical features and further pass these numerical features to the machine learning algorithms.

As we all know that the raw text stored in some dump repository contains a lot of meaningful information. And in today’s fast changing world, it becomes essential to consider data driven decision than fully rely on experience driven decision. ## Data Visualization using plotly, matplotlib, seaborn and squarify

Data Visualization is one of the important activity we perform when doing Exploratory Data Analysis. It helps in preparing business reports, visual dashboards, story telling etc important tasks. In this post I have explained how to ask questions from the data and in return get the self explanatory graphs. In this You will learn the use of various python libraries like plotly, matplotlib, seaborn, squarify etc to plot those graphs.