This post( part 5) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts.

Welcome back peeps. This post( part 3) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts.

I hope you all are enjoyed reading my earlier article Part – I 10/20, and I trust that would be useful for you. Let's discuss the rest of the project quickly. When you re dealing with NLP based problem statement, we must focus on Text Data preparation before you can start using it for any NLP algorithm. The foremost step is that text cleaning and processing is an important task in every machine learning project, even if we are working on the text-based task and making sense of textual data. So, when dealing with text, we must take extra causes for Text Classification, Text Summarization, understanding Tokenization, and Bag of Words preparation.

I hope you all are enjoyed reading my earlier article Part - I 10/20, and I trust that would be useful for you. Let's discuss the rest of the project quickly. When you're dealing with NLP based problem statement, we must focus on "Text Data" preparation before you can start using it for any NLP algorithm. The foremost step is that text cleaning and processing is an important task in every machine learning project, even if we are working on the text-based task and making sense of textual data. So, when dealing with text, we must take extra causes for Text Classification, Text Summarization, understanding Tokenization, and Bag of Words preparation.

Description -- This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.

In this article, I will introduce you to more than 180 data science and machine learning projects solved and explained using the Python programming language. I hope you liked this article on more than 180 data science and machine learning projects solved and explained by using the Python programming language. Feel free to ask your valuable questions in the comments section below.

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions. In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action. Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.

According to Stanford Researcher, John McCarthy, "Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." Simply put, AI's goal is to make computers/computer programs smart enough to imitate the human mind behaviour. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings.

Onboarding AI to products does not always go that smoothly. Some might say it could be either success, or failure. But more likely - first results will be useless, and only after several iterations there’d be actual value. So here are stories of what went wrong with AI projects, while evolving. And how eventually businesses managed to set things right. Despite how many books are read, at the end of the day we all learn from our own experience. But, perhaps, those stories, and takeaways, will put at least some pillows for us to land on!

Description -- This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.