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Course introduction

This video will introduce the topics of this week.
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Hello everybody, welcome to the “AI for legal professionals” course. This is the second half of a two-part series, you don’t need to take them consecutively, you can start with this second half, that’s okay. So in this first week, we’ll talk about, we give a basic introduction to artificial intelligence, and machine learning, and talk about data mining, and legal analytics, to give a broad overview of what we’re talking about. Then we’ll talked about the software tools, that are used to create these tools. In specific, we will talk about python, Pytorch, TensorFlow, R, and software tools, called Rapidminer, and Tableau.
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Then we will talk about Colaboratory, colab is a Google product, and the Jupyter Notebook, and we’ll talk about the Jupyter Notebook, and Github environments. Now this first half might be already familiar to you, which is okay if it’s not, we just want to make sure that everybody’s on the same page for this course. So talk about the basic themes, the tools using programming to create these legal tools. Then we’ll shift into data mining, and we will focus on data mining in this space, so focusing on information extraction from databases text, how are we getting information from text or from the world and using it for our purposes, that’s useful for us.
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Then we’ll really talk about the data collection process, the data preparation process, model development, a model evaluation, pose processing, and model deployment. This is basically referred to as the life cycle, model development life cycle. Then we’ll talk about classifying, clustering identifying themes in text, topic modelling, and other kinds of visualization when we refer to the what where when why how how much of the legal profession. Then we will wrap up the week with a discussion of eDiscovery, eDisclosure, compliance, and due diligence. So what is this?
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This is basically when we are trying to find out about the body of data and kind of a forensic way, so in the United States, we have this idea of discovery in the legal process, which is just the exchange of relevant documents relating to litigation, and basically this is done now through electronic means, because most things are stored electronically, documents, emails, text messages, those sorts of things are generally in electronic form. It’s not always the case there are definitely companies and people storing hard copies of documents, but in general things are being stored electronically. So good luck, that’s all I’d like to talk about in this introductory video. Thanks everybody and take care.

Hello everybody! Welcome to this course. I’m Mark Shope, the main instructor for this course, and you will be hearing from me in each section.

This is the second half of a two-part series. If you didn’t take the first half, don’t worry, you still can start with this course.

If you’re interested, welcome to enroll the first course: AI for Legal Professionals (I): Law and Policy as well.

This is our first week. In this week, we will discuss these topics:

  1. Introduction to Artificial Intelligence, Machine Learning, Data Mining, and Legal Analytics.

  2. Introduction to Python, PyTorch, TensorFlow, R Programming, RapidMiner, and Tableau.

  3. Introduction to the Colaboratory, Jupyter Notebook, and GitHub Environments.

  4. Data mining in law: Information extraction from legal databases and texts; data collection, data preparation, model development, model evaluation and postprocessing, and model deployment.

  5. Data mining in law: Classifying, clustering, and identifying themes in data; argument mining from legal texts; topic modelling.

  6. Data mining in law: eDiscovery, eDisclosure, Compliance, Due Diligence.

Please feel free to share your ideas and learn with us.

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AI for Legal Professionals (II): Tools for Lawyers

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