How Machine Learning changing the traditional ways of developing a mobile app

The term Machine Learning was given by Arthur Samuel in the year 1959 and he defined it as the “field of study that gives computers the ability to learn without being explicitly programmed”. Now Machine Learning is helping organizations to take data driven decisions rather completely relying on the experience driven decisions.

Further in this article, we will discuss the benefits of Machine Learning in the development process of a mobile app and everything which revolves around Machine Learning and its use in developing a mobile app.

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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.

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.

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.

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