Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

 

Get to grips with the LangChain framework from theory to deployment and develop production-ready applications.

Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments.

Purchase of the print or Kindle book includes a free PDF eBook.

Key Features

Learn how to leverage LLMs' capabilities and work around their inherent weaknesses

Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges

Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Book Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.

Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

What you will learn

Understand LLMs, their strengths and limitations

Grasp generative AI fundamentals and industry trends

Create LLM apps with LangChain like question-answering systems and chatbots

Understand transformer models and attention mechanisms

Automate data analysis and visualization using pandas and Python

Grasp prompt engineering to improve performance

Fine-tune LLMs and get to know the tools to unleash their power

Deploy LLMs as a service with LangChain and apply evaluation strategies

Privately interact with documents using open-source LLMs to prevent data leaks

Who this book is for

The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.

Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.

Table of Contents

What Is Generative AI?

LangChain for LLM Apps

Getting Started with LangChain

Building Capable Assistants

Building a Chatbot like ChatGPT

Developing Software with Generative AI

LLMs for Data Science

Customizing LLMs and Their Output

Generative AI in Production

The Future of Generative Models

Hard Copy: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

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Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

 

Printed in Color

Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact

Leverage the power of Generative AI with Kaggle Models

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models

Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound

Improve the style and readability of your Notebooks, making them more impactful and compelling

Book Description

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.

For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable.

Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.

What you will learn

Approach a dataset or competition to perform data analysis via a notebook

Learn data ingestion and address issues arising with the ingested data

Structure your code using reusable components

Analyze in depth both small and large datasets of various types

Distinguish yourself from the crowd with the content of your analysis

Enhance your notebook style with a color scheme and other visual effects

Captivate your audience with data and compelling storytelling techniques

Who this book is for

This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to:

Beginners on Kaggle from any background

Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization

Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings

Professionals who already use Kaggle for learning and competing

Table of Contents

Introducing Kaggle and Its Basic Functions

Getting Ready for Your Kaggle Environment

Starting Our Travel - Surviving the Titanic Disaster

Take a Break and Have a Beer or Coffee in London

Get Back to Work and Optimize Microloans for Developing Countries

Can You Predict Bee Subspecies?

Text Analysis Is All You Need

Analyzing Acoustic Signals to Predict the Next Simulated Earthquake

Can You Find Out Which Movie Is a Deepfake?

Unleash the Power of Generative AI with Kaggle Models

Closing Our Journey: How to Stay Relevant and on Top

Hard Copy: Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

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IBM AI Foundations for Business Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from IBM

Join Free: IBM AI Foundations for Business Specialization

Specialization - 3 course series

This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.  

Applied Learning Project

Each of the courses in this specialization include Checks for Understanding, which are designed to assess each learner’s ability to understand the concepts presented as well as use those concepts in actual practice.  Specifically, those concepts are related to introductory knowledge regarding 1) artificial intelligence; 2) data science, and; 3) the AI Ladder.  

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Evaluations of AI Applications in Healthcare

 

What you'll learn

Principles and practical considerations for integrating AI into clinical workflows

Best practices of AI applications to promote fair and equitable healthcare solutions

Challenges of regulation of AI applications and which components of a model can be regulated

What standard evaluation metrics do and do not provide

Join Free: Evaluations of AI Applications in Healthcare

There are 7 modules in this course

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

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Generative AI: Enhance your Data Analytics Career

 

What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

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Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

 

Unlock the Future of AI!

Delve into the world of Generative AI with Daneyal Anis' groundbreaking book, "The Ultimate Step by Step Guide to ChatGPT Using Python". If you've ever been intrigued by how machine learning, data science, and artificial intelligence can be harnessed for tangible results, this guide is your key.

In today's digital age, the fields of Artificial Intelligence (AI), Machine Learning (ML), and Data Science are not just buzzwords; they are the foundational pillars that drive innovations across industries. From big tech giants to emerging startups, AI-powered solutions are the backbone of breakthroughs.

Here's what you'll discover within this comprehensive guide:

How the union of Python, the most popular language in data science, and GPT is revolutionizing the tech space.

Deep dives into the power and potential of GPT - learning its strengths, nuances, and applications.

Strategies for monetizing your AI and ML skills, unveiling the golden opportunities that await in the AI space.

Building robust AI portfolios and utilizing automation tools for efficiency and scalability.

Crafting AI profiles, including creating dynamic chatbots using ChatGPT.

Navigating the ethical considerations and responsibilities in the AI domain.

Beyond just the knowledge, this guide is crafted to action. That's why Daneyal also offers an exclusive 90-Day Plan to make passive income using Generative AI, leading you from the theoretical to practical monetization of your skills. Plus, get exclusive access to an in-depth Step by Step Course for those wanting a hands-on learning experience.

Editorial Reviews

The Digital Era is here, and AI is at its forefront. Equip yourself with the knowledge, tools, and strategies to not only participate in this revolution but also to thrive and lead. With "The Ultimate Step by Step Guide to ChatGPT Using Python", your transformative journey in the realm of AI is set on a promising path.

Hard Copy: Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

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Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

 

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python

Key Features

Get up and running with artificial intelligence in no time using hands-on problem-solving recipes

Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images

Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more

Book Description

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.

Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.

By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.

What you will learn

Implement data preprocessing steps and optimize model hyperparameters

Delve into representational learning with adversarial autoencoders

Use active learning, recommenders, knowledge embedding, and SAT solvers

Get to grips with probabilistic modeling with TensorFlow probability

Run object detection, text-to-speech conversion, and text and music generation

Apply swarm algorithms, multi-agent systems, and graph networks

Go from proof of concept to production by deploying models as microservices

Understand how to use modern AI in practice

Who this book is for

This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Table of Contents

Getting Started with Artificial Intelligence in Python

Advanced Topics in Supervised Machine Learning

Patterns, Outliers, and Recommendations

Probabilistic Modeling

Heuristic Search Techniques and Logical Inference

Deep Reinforcement Learning

Advanced Image Applications

Working with Moving Images

Deep Learning in Audio and Speech

Natural Language Processing

Artificial Intelligence in Production

Hard Copy: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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Generative AI Essentials: Overview and Impact

 

What you'll learn

Learn how generative AI works

Explore the benefits and drawbacks of generative AI

Learn how generative AI can integrate into our daily lives

Join Free: Generative AI Essentials: Overview and Impact

There is 1 module in this course

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like. This course brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI on our current society and its implications for the future.

This course is licensed CC BY-SA 4.0 with the exclusion of the course image.

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Introduction to AI in the Data Center

 

What you'll learn

What is AI and AI use cases, Machine Learning, Deep Leaning, and how training and inference happen in a Deep Learning Workflow.

The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.    

Become familiar with deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem or cloud.

Requirements for multi-system AI clusters and considerations for infrustructure planning, including servers, networking, storage and tools. 

Join Free: Introduction to AI in the Data Center

There are 4 modules in this course

Welcome to the Introduction to AI in the Data Center Course!

As you know, Artificial Intelligence, or AI, is transforming society in many ways. 

From speech recognition to improved supply chain management, AI technology provides enterprises with the compute power, tools, and algorithms their teams need to do their life’s work. 

But how does AI work in a Data Center? What hardware and software infrastructure are needed? 

These are some of the questions that this course will help you address. 

This course will cover an introduction to concepts and terminology that will help you start the journey to AI and GPU computing in the data center. 

You will learn about:

* AI and AI use cases, Machine Learning, Deep Learning, and how training and inference happen in a Deep Learning Workflow. 

* The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.

* Deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment. ​ 

* Requirements for multi-system AI clusters​​, considerations for infrastructure planning, including servers, networking, and storage and tools for cluster management, monitoring and orchestration. 

This course is part of the preparation material for the NVIDIA Certified Associate - ”AI in the Data Center” certification. 

This certification will take your expertise to the next level and support your professional development.

Who should take this course?

* IT Professionals

* System and Network Administrators

* DevOps

* Data Center Professionals

No prior experience required.

This is an introduction course to AI and GPU computing in the data center. 

To learn more about NVIDIA’s certification program, visit: 

https://academy.nvidia.com/en/nvidia-certified-associate-data-center/

So let's get started!

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CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

 

What you'll learn

Learn about the business problems that AI/ML can solve as well as the specific AI/ML technologies that can solve them.  

Learn important tasks that make up the workflow, including data analysis and model training and about how machine learning tasks can be automated. 

Use ML algorithms to solve the two most common supervised problems regression and classification, and a common unsupervised problem: clustering.

Explore advanced algorithms used in both machine learning and deep learning. Build multiple models to solve business problems within a workflow.

Join Free:CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Professional Certificate - 5 course series

The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP. 

AI and ML have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. 

The specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam. 

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work.  Projects include: 

Create an AI project outline

Follow a machine learning workflow to predict demand 

Build a regression, classification, or clustering model

Build a convolutional neural network (CNN)

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Artificial Intelligence (AI) Education for Teachers

 

What you'll learn

Compare AI with human intelligence, broadly understand how it has evolved since the 1950s, and identify industry applications

Identify and use creative and critical thinking, design thinking, data fluency, and computational thinking as they relate to AI applications

Explain how the development and use of AI requires ethical considerations focusing on fairness, transparency, privacy protection and compliance

Describe how thinking skills embedded in Australian curricula can be used to solve problems where AI has the potential to be part of the solution

Join Free:Artificial Intelligence (AI) Education for Teachers

There are 6 modules in this course

Today’s learners need to know what artificial intelligence (AI) is, how it works, how to use it in their everyday lives, and how it could potentially be used in their future. Using AI requires skills and values which extend far beyond simply having knowledge about coding and technology.

This course is designed by teachers, for teachers, and will bridge the gap between commonly held beliefs about AI, and what it really is. AI can be embedded into all areas of the school curriculum and this course will show you how. 

This course will appeal to teachers who want to increase their general understanding of AI, including why it is important for learners; and/or to those who want to embed AI into their teaching practice and their students’ learning. There is also a unique opportunity to implement a Capstone Project for students alongside this professional learning course.

Macquarie School of Education at Macquarie University and IBM Australia have collaborated to create this course which is aligned to AITSL ‘Proficient Level’ Australian Professional Standards at AQF Level 8.

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Artificial Intelligence on Microsoft Azure

 

What you'll learn

How to identify guiding principles for responsible AI

How to identify features of common AI workloads

Join Free:Artificial Intelligence on Microsoft Azure

There is 1 module in this course

Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.

In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI. You’ll see some of the ways that AI can be used and explore the principles of responsible AI that can help you understand some of the challenges facing developers as they try to create ethical AI solutions. 

This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the first course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains.  This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them.

This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.  To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology,  general technology concepts, including concepts of machine learning and artificial intelligence.

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AI, Business & the Future of Work

 

What you'll learn

How AI can give you better information upon which to make better decisions.

How AI can help you automate processes and become more efficient.

How AI will impact your industry so that you can avoid the pitfalls and seize the benefits.

Join Free:AI, Business & the Future of Work

There are 4 modules in this course

This course from Lunds university will help you understand and use AI so that you can transform your organisation to be more efficient, more sustainable and thus innovative. 

The lives of people all over the world are increasingly enhanced and shaped by artificial intelligence. To organisations there are tremendous opportunities, but also risks, so where do you start to plan for AI, business and the future of work?

Whether you are in the public or private sector, in a large organisation or a small shop, AI has a growing impact on your business. Most organisations don’t have a strategy in place for how to make AI work for them. 

The teacher, Anamaria Dutceac Segesten, will guide you through the topics with short lectures, interviews and interactive exercises meant to get you thinking about your own context.

12 industry professionals, AI experts and thought leaders from different industries have been interviewed and will complement the short lectures to give you a broad overview of perspectives on the topics. You will meet:

Kerstin Enflo

Professor in Economic History

Lund University

Dr. Irene Ek

Founder

Digital Institute

Samuel Engblom

Policy Director

The Swedish Confederation of Professional Employees

Pelle Kimvall

Lead Solution Ideator

AFRY X

Joakim Wernberg

Research Director, Digitalisation and Tech Policy

Swedish Entrepreneurship Forum

Marcus Henriksson

Empathic Leader of AI & Automation and Digital Business Development

Empathic

Johan Grundström Eriksson

Board Advisor, Innovation Management & Corporate Governance

Founder & Chairman, aiRikr Innovation AB

Jakob Svensson

Professor in Media and Communication Studies

Malmö University

Ulrik Franke

Senior Researcher

RISE Research Institutes of Sweden

Björn Lorentzon

Nordic Growth Lead

Sympa

Anna Felländer

Founder 

AI Sustainability Center

Prof. Fredrik Heintz

Associate Professor of Computer Science

Linköping University

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AI Product Management Specialization

 

What you'll learn

Identify when and how machine learning can applied to solve problems

Apply human-centered design practices to design AI product experiences that protect privacy and meet ethical standards

Lead machine learning projects using the data science process and best practices from industry

Join Free:AI Product Management Specialization

Specialization - 3 course series

Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems.  This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.

This Specialization provides a foundational understanding of how machine learning works and when and how it can be applied to solve problems.  Learners will build skills in applying the data science process and industry best practices to lead machine learning projects, and develop competency in designing human-centered AI products which ensure privacy and ethical standards. The courses in this Specialization focus on the intuition behind these technologies, with no programming required, and merge theory with practical information including best practices from industry.  Professionals and aspiring professionals from a diverse range of industries and functions, including product managers and product owners, engineering team leaders, executives, analysts and others will find this program valuable.   

Applied Learning Project

Learners will implement three projects throughout the course of this Specialization:

1) In Course 1, you will complete a hands-on project where you will create a machine learning model to solve a simple problem (no coding necessary) and assess your model's performance.

2) In Course 2, you will identify and frame a problem of interest, design a machine learning system which can help solve it, and begin the development of a project plan.

3) In Course 3, you will perform a basic user experience design exercise for your ML-based solution and analyze the relevant ethical and privacy considerations of the project.

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AI Foundations for Everyone Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from IBM

Join Free:AI Foundations for Everyone Specialization

Specialization - 4 course series

Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance. 

 This specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills. It is designed to give you a firm understanding of what is AI, its applications and use cases across various industries. You will become acquainted with terms like Machine Learning, Deep Learning and Neural Networks. 

Furthermore, it will familiarize you with IBM Watson AI services that enable any business to quickly and easily employ pre-built AI smarts to their products and solutions. You will also learn about creating intelligent virtual assistants and how they can be leveraged in different scenarios.

 By the end of this specialization, learners will have had hands-on interactions with several AI environments and applications, and have built and deployed an AI enabled chatbot on a website – without any coding. 

Applied Learning Project

Learners will perform several no-code hands-on exercises in each of the  three courses. At the end of the last course, learners would have developed,  tested, and deployed a Watson AI powered customer service chatbot on a website to delight their clients.

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AI For Business Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from University of Pennsylvania

Join Free:AI For Business Specialization

Specialization - 4 course series

This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business.

By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. 

For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor 

Applied Learning Project

Each course module in this Specialization culminates in an assessment, with two courses including peer-review exercises. These assessments are designed to check learners' knowledge and to provide an opportunity for learners to apply course concepts such as data analytics, machine learning tools, and people management best practices with AI algorithms.

The assessments will be cumulative and cover the application of artificial intelligence, ethical governance rules, Big Data management, the customer journey, fraud prevention, and personalization technology in order to develop and implement a successful AI strategy for your business.

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Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate

 

What you'll learn

Manage Azure resources for machine learning

Run experiments and train models

Deploy and operationalize ethical machine learning solutions

Join Free:Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate

Professional Certificate - 5 course series

This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. This Professional Certificate teaches learners how to create end-to-end solutions in Microsoft Azure. They will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at cloud scale using Azure Machine Learning. This Professional Certificate teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this program, you will be ready to take the DP-100: Designing and Implementing a Data Science Solution on Azure.

Applied Learning Project

Learners will engage in interactive exercises throughout this program that offers opportunities to practice and implement what they are learning. They will work directly in the Azure Portal and use the Microsoft Learn Sandbox. This is a free environment that allows learners to explore Microsoft Azure and get hands-on with live Microsoft Azure resources and services.

For example, when you learn about training a deep neural network; you will work in a temporary Azure environment called the Sandbox. The beauty about this is that you will be working with real technology but in a controlled environment, which allows you to apply what you learn, and at your own pace.

You will need a Microsoft account. If you don't have one, you can create one for free. The Learn Sandbox allows free, fixed-time access to a cloud subscription with no credit card required. Learners can safely explore, create, and manage resources without the fear of incurring costs or "breaking production".

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AI for Medicine Specialization

 

What you'll learn

Diagnose diseases from x-rays and 3D MRI brain images

Predict patient survival rates more accurately using tree-based models

Estimate treatment effects on patients using data from randomized trials

Automate the task of labeling medical datasets using natural language processing

Join Free:AI for Medicine Specialization

Specialization - 3 course series

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases.  If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the 

Deep Learning Specialization

Applied Learning Project

Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You’ll start by learning the nuances of working with 2D and 3D medical image data. You’ll then apply tree-based models to improve patient survival estimates. You’ll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you’ll explore how natural language extraction can more efficiently label medical datasets.

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Data Privacy Fundamentals

 

What you'll learn

Identify foundational understanding of digital age privacy concepts and theories

Identify privacy implications of modern digital technology

Identify the rules and frameworks for data privacy in the age of technology

Join Free:Data Privacy Fundamentals

There are 3 modules in this course

This course is designed to introduce data privacy to a wide audience and help each participant see how data privacy has evolved as a compelling concern to public and private organizations as well as individuals. In this course, you will hear from legal and technical experts and practitioners who encounter data privacy issues daily. This course will review theories of data privacy as well as data privacy in the context of social media and artificial intelligence. It will also explore data privacy issues in journalism, surveillance, new technologies like facial recognition and biometrics. Completion of the course will enable the participant to be eligible for CPE credit.

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Industrial IoT Markets and Security

 

What you'll learn

What Industry 4.0 is and what factors have enabled the IIoT.

Key skills to develop to be employed in the IIoT space.

What platforms are, and also market information on Software and Services.

What the top application areas are (examples include manufacturing and oil & gas).

Join Free:Industrial IoT Markets and Security

There are 5 modules in this course

This course can also be taken for academic credit as ECEA 5385, part of CU Boulder’s Master of Science in Electrical Engineering degree.

Developing tomorrow's industrial infrastructure is a significant challenge. This course goes beyond the hype of consumer IoT to emphasize a much greater space for potential embedded system applications and growth: The Industrial Internet of Things (IIoT), also known as Industry 4.0. Cisco’s CEO stated: “IoT overall is a $19 Trillion market. IIoT is a significant subset including digital oilfield, advanced manufacturing, power grid automation, and smart cities”.

This is part 1 of the specialization. The primary objective of this specialization is to closely examine emerging markets, technology trends, applications and skills required by engineering students, or working engineers, exploring career opportunities in the IIoT space. The structure of the course is intentionally wide and shallow: We will cover many topics, but will not go extremely deep into any one topic area, thereby providing a broad overview of the immense landscape of IIoT. There is one exception: We will study security in some depth as this is the most important topic for all "Internet of Things" product development.

In this course students will learn :

  * What Industry 4.0 is and what factors have enabled the IIoT

  * Key skills to develop to be employed in the IIoT space

  * What platforms are, and also market information on Software and Services

  * What the top application areas are (examples include manufacturing and oil & gas)

  * What the top operating systems are that are used in IIoT deployments

  * About networking and wireless communication protocols used in IIoT deployments

  * About computer security; encryption techniques and secure methods for insuring data integrity and authentication

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