Machine Learning and Deep Learning in Python Using Keras and TensorFlow

Machine learning has become immensely popular with the abundance of big data and the computing power available to process it.

It enables you to solve complex problems, such as identifying speech and text using Natural Language Processing (NLP), as well as identifying objects in images.

In this article, we look at how implementing machine learning and deep learning in Python using Keras and TensorFlow helps you power decision-making with data.

So, let’s get started!

Linear Regression

This is a model that helps you understand the relationship between input and output numerical variables.

It assumes a linear relationship between the input variables (x) and the single output variable (y).

Here’s what linear regression helps you with:

• Predicting weather on the basis of input features
• Conducting market research and analyzing customer surveys

Logistic Regression

This model helps you calculate the probability of an event occurring on the basis of data that you have previously provided.

Being a measure of probability, the output lies in the range of 0-1.

Here’s what logistic regression can be used for:

• Measuring the probability of a customer liking a recommended product
• Labeling email as spam, or otherwise, by identifying frequently used keywords
• Classifying a training example as belonging to a given species (probability = 1), or not (probability = 0)

Support Vector Machines (SVM)

An SVM classifier separates two classes as far away from each other as possible.

Here’s what Support Vector Machines help you with:

• Classifying parts of an image as being part of a face, or not
• Getting better search accuracy for image classification
• Recognizing widely used handwritten characters

K-Nearest Neighbors (KNN)

The KNN algorithm classifies data according to the ‘similarity’ of data points.

Here’s what K-Nearest Neighbors (KNN) can be used for:

• Predicting loan default on the basis of transactional attributes
• Predicting weather on the basis of temperature, humidity, atmospheric pressure, etc.
• Predicting fraud on the basis of bills submitted for employee reimbursement

Neural Networks

Neural networks enable you to group unlabeled data together by identifying similarities in input data and classifying labeled data into classes.

Here’s what neural networks can be used for:

• Compressing images
• Predicting stock prices
• Solving high-dimensional control problems with reinforcement learning
• Predicting real estate prices

Wrapping Up

In this article, we looked at how implementing machine learning and deep learning in Python using Keras and TensorFlow helps you power decision-making with data.

Machine learning enables you to understand customer behavior, build recommender systems, detect fraud, and more.

Implementing Machine Learning in Python Using Keras and TensorFlow