What do the industries of Finance, Banking, Gaming, Construction, Manufacturing, and numerous others have in common? Data. They all collect, store, and analyze data to sharpen the quality of products and services. Every industry is utilizing and maximizing their respective data operations. Through the course of the last thirteen chapters, we focused on learning Python for implementing AI based processes and solved various real-world problems using AI. Computer Systems, and more specifically AI systems change every day as the learning never stops. Therefore, this series of chapters was an introduction to the field of artificial intelligence. We approached prerequisites in the form of mathematical formulae and statistics that are very important for building analytical models in AI, then later moved to machine learning, and built various algorithms like NLP on top of Machine Learning systems. We will use this chapter to talk about how AI is currently dominating the market and how it has changed in this era, since its origin.

AI Services Artificial Intelligence Cloud AI Python

The world is moving towards Technology, and the growth of computing has been tremendous in the past decade. All business domains from energy to healthcare, are dependent on technology for their sustenance. A promising rise in this field has been Artificial Intelligence that is now becoming a staple software development technique. Throughout this series we will be concentrating on learning Programming using Python and focus on using Python for building Intelligent applications.

AI Artificial Intelligence python

In the previous chapter we saw how Artificial Intelligence is evolving in today’s world and how programmers are building intelligent machines. We also understood the importance of connecting statistics and code to achieve this goal. This section of the series focuses on the different kinds of data we would go through when programming in Python and the respective variables that would store this data. Towards the end we will also look at creating some constructs that help in gathering and manipulating numerical data through Python’s in-built libraries.

AI Artificial Intelligence Python Statistics

This chapter will show the implementation of Natural Language Processing through a case study that deals with sentiments among flight travelers. In the first case study, we built a machine learning pipeline that ran through data acquiring to performing predictions. In this analysis, we will be presenting the same pipeline and adding an additional step to work with textual data. We will also be using the libraries talked about in the previous chapter and learn concepts of vectorizing text data to numerical data. The aim of Natural Language Processing (NLP) is to work with textual (string–based) data and perform analysis on it. In this chapter, we will focus on making predictions and building categorizations from input feeds of data.

AI Artificial Intelligence Natural Language Processing NLP Python

Speech is the primary and most basic form of human communication and is therefore a vital part of understanding human behavior. Speech Recognition in Artificial Intelligence is a technique deployed on computer programs to enable them in understanding human language in the form of spoken words. With our learnings of Natural Language Processing (NLP) in the last few chapters, we will now transition towards understanding speech–based cognitive recognition systems. In this chapter, we will concentrate on Speech Recognition

Artificial Intelligence Python Speech Recognition

Through the last four chapters of the series, we have focused on working with languages, speech, and images. These are the core of computer vision concepts. As we move deeper into image processing with the aim of learning about human behavior, in this chapter we will concentrate on working with Computer Vision. The idea is to replicate the human thought process based on training data (in the form of images and videos of humans) and try to segment the emotions present in this data. To perform our analysis in this chapter we will be concentrating on pre–recorded images and videos that showcase an emotion, but the same can also be implemented on a live stream of video feed for real–time analytics. As we move forward in this chapter and the next one, we will understand concepts like human cognition, sentiment analysis through facial expressions and various libraries and frameworks that help in this execution. Before we move further, let us try to understand what Computer Vision is and what roles it plays in the Artificial Intelligence universe.

AI Artificial Intelligence Computer Vision Python

We have now reached the concluding chapter of our machine learning journey. This is also the beginning of the first project and will focus on Machine Learning for Problem Solving. We will take a real-life problem and try solving it using ML algorithms. To compute the best-case solution, we will also perform significant amount of data preprocessing and cleaning to make it ready for analysis.

AI Artificial Intelligence Machine Learning ML Python

We started our first case of study in machine learning. We worked with the Ames Housing dataset and we applied the EDA and some statistics tests on the data. Let's now continue our analysis.

AI Artificial Intelligence Machine Learning ML Python

We started Artificial Intelligence with Machine Learning and Data Analysis. In the last six chapters, we moved from basics of Machine Learning to solving a real-world example of predicting house prices based on specified features. AI and ML go hand in hand within projects and it is now time for us to move ahead in this journey. In the coming chapters we will be concentrating on a diverse set of Artificial Intelligence concepts revolving around cognitive science (speech, writing, etc.)

AI Artificial Intelligence Logic Programming Python