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The future is already here — it’s just not very evenly distributed. - William Gibson


How to Monetize AI and Machine Learning Solutions

3 new jobs A.I. is creating: Trainers, explainers, and sustainers | Paul Daugherty
Smarter Faster™ Big Think is the leading source of expert-driven, actionable, educational content -- with thousands of videos, featuring experts ranging from Bill Clinton to Bill Nye, we help you get smarter, faster. S​ubscribe to learn from top minds like these daily. Get actionable lessons from the world’s greatest thinkers & doers. Our experts are either disrupting or leading their respective fields. ​We aim to help you explore the big ideas and core skills that define knowledge in the 21st century, so you can apply them to the questions and challenges in your own life.

SuperBot 2018: Monetization Strategies for Chatbots and Voice Apps
The million dollar question in the chatbot industry is "How do you monetize conversational products?" Hear the answer from our expert panelists. Speakers: Bree Glaeser (The Mars Agency), Lauren Kunze (Pandorabots), Peter Buch (Swell), Ben Parr (Octane AI), Moderated by: Stefan Kojouharov (Chatbot’s Life) Sign up to get analytics for your bot:

Lessons from Founder Dennis Mortensen - Building An Artificial Intelligence Startup is an NYC based artificial intelligence company that’s raised over $30 million. I sat down with the founder Dennis Mortensen to learn more about how they’re positioning and selling the product to the enterprise. Talking to him got me super curious about the rise of artificial intelligence, and I hope this interview does similar for you.

The Power of Machine Learning and The Opportunity for Monetization
Learn more about AWS for Media & Entertainment at – From the 2019 NYC M&E Symposium: As interest in AI and ML continues to grow, we have seen many companies investigate the use of machine learning to help transform their content operations. By leveraging the power of this technology, Videofashion is able to find targeted content within 18,000 hours of fashion-focused television programming and videos using Curio™, by GrayMeta. With this rich library, Videofashion has teamed up with GrayMeta to provide an innovative platform which delivers deep insights into all their content, making it accessible and searchable online. Join GrayMeta and Videofashion along with AWS to further discuss machine learning and the monetization of assets.

How To Make Money in (2020) With AI and Machine Learning || startup ideas for AI and ML
Video contains about the 9 startup ideas based on AI and ML which can be started by any individual who is learning and working on Machine Learning and Artificial Intelligence. video describes about the different industry where you can start your journey as a entrepreneur in 2020.

7 Ways to make money using Machine Learning and AI | Make MONEY from Machine Learning
This video titled "7 Ways to make money using Machine Learning and AI | Make MONEY from Machine Learning" explains 7 ways to make money using Machine Learning and AI (artificial intelligence). Opportunities are endless and there are a lot of ways using which people like students, professionals, teachers who has acquired knowledge in this field can earn a lot of money.

Watch Me Build an AI Startup
I'm going to build a medical imaging classification app called SmartMedScan! The potential customers for this app are medical professionals that need to scale and improve the accuracy of their diagnoses using AI. From ideation, to logo design, to integrating features like payments and AI into a single app, I'll show you my 10 step process. I hope that by seeing my thought process and getting familiar with the sequence of steps I'll demonstrate, you too will be as inspired as I am to use this technology to do something great for the world. Enjoy!

How to Start an AI Startup
How are you supposed to get in on the AI hype? Deep learning has enabled a whole new breed of applications, and there are still so many different opportunities to apply it in fields that are completely untapped. I'll go through the steps you need to take to start your own AI startup using a combination of my own experiences and best practices from the industry as a guide. From data collection to model training to picking a problem, we'll try to understand this challenging task.

Make Money with Tensorflow 2.0
I've built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used TensorFlow Serving + Flask to create a simple web app around it. I'll explain what the important parts you should know in Tensorflow 2.0 are, then I'll guide you through my code & thought process of building an AI startup using it. Enjoy!

7 Ways to Make Money with Machine Learning
Machine Learning is an amazing technology, but how are you supposed to earn a living from it? In this video, I'll break down 7 ways that anyone can earn money from anywhere in the world using machine learning. Well start by taking a look at whats called the "AI Value Chain" to learn who is currently making money in machine learning so that we can better chart out where we can contribute to the space. From startups, to competitions, to writing books, we've got a lot to cover in this video. Enjoy!

How to Do Freelance AI Programming
You can build a sustainable full-time income from doing freelance AI programming work. In this video, i'm going to show you the steps you can take to start your journey as a freelancer. Whether you're a student or are employed full-time, you can begin the process of planning out a freelance career today. Getting clients, leveling up your skills, marketing yourself, setting up your financials, tools to help optimize your workflow, these are all aspects of the freelance life that i'll explain from my own personal experience.

How to Make Money as a Programmer in 2018
I'll go through 5 methods that you can use to make money as a programmer! We are lucky in that our skill will only get more valuable to society over time. Links to everything I've discussed are below.

Learning Approaches

The Fastest Path To Deep Learning
Learning Deep Learning can be confusing and often very frustrating. In this talk, Sam will set out a roadmap to go from knowing nothing to being fluent in Deep Learning in the fastest way possible. He will highlight courses, frameworks, math, methods, and strategies to get you started and set you on the path to being able to use Deep Learning for real worlds problems and apps. EVENT: FOSSASIA 2018 SPEAKER: Sam Witteveen, Machine Learning Developer Expert Google

How I'm Learning AI and Machine Learning
For the past 6 months or so, I have been teaching myself about artificial intelligence. In this video, I describe some of the places I learned from and a few of the things I've done with my new found knowledge. Lots of my AI code:

How to get started in machine learning - best books and sites for machine learning
This is a short video which gives advice on how to learn Machine Learning. Machine learning is an extremely exciting field, there is so much to it and it can be a little overwhelming for someone new trying to enter the field. This video will guide you on how to start, where to find the right resources and on the path you should take. It recommends online courses, websites and books. If you have any questions about Machine Learning, please put them in the comments section. Machine Learning is a term that covers quite a large field. It uses linear regression, logistic regression, classification, deep learning, support vector machines and neural networks. It is a branch of data science and is a very exciting field. One of its main applications is in artificial intelligence.

How to Learn Deep Learning (when you’re not a computer science PhD)
Talk video from meetup April 11, 2017 at AWS office in SF. Huge thanks to Amazon for providing venue, food/drink, and video recording! Abstract: Many people claim that deep learning needs to be a highly exclusive field, saying that you must spend years studying advanced math before you even begin to attempt it. Jeremy Howard and I believed that this was just not true, so we set out to see if we could teach deep learning to coders (with no math prerequisites) in 7 part-time weeks. Our students are now using deep learning to identify chainsaw noise in endangered rain forests, create translation resources for Pakistani languages, reduce farmer suicides in India, diagnose breast cancer, and more. We wanted to help them get results fast, so we taught them in a code-centric, application-focused way. I’ll share what we learnt about how to learn deep learning effectively, so that you can set out on your own learning journey. [[Creatives#Rachel Thomas|Rachel Thomas]

Learn Machine Learning in 3 Months (with curriculum)
How is a total beginner supposed to get started learning machine learning? I'm going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the deep learning theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

> How to Study Machine Learning
Let me show you the techniques I use to study machine learning in this video. That includes living a healthy lifestyles, optimizing your learning environment, creating a personalized learning path, prioritizing effectively, and being an active learner. I'll demo the FAST technique, which you can use to help learn faster and more efficiently. I made this with machine learning technology in mind, but these techniques can be used for any field. Enjoy!

Machine Learning Roadmap | Daniel Bourke

2020 Machine Learning Roadmap
Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction. Although the title of this video says machine learning roadmap, you should treat it as a compass. Explore it, follow your curiosity, learn something and use what you learn to create your next steps. Links: Interactive Machine Learning Roadmap -

Starting a Career

Why Data Science Skill-Building Might Be Holding You Back | CareerCon 2019 | Kaggle
Dan Becker, Head of Kaggle Learn, makes a strong case against obsessive learning, and instead argues for a focused game plan for finding your first data science job. Hosted by Addison Howard. About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spin up a Jupyter notebook with a single click. Build with our huge repository of free code and data. Stumped? Ask the friendly Kaggle community for help.

How to be Ready for Jobs in an Artificial Intelligence Driven World
The world is changing and is changing fast. Many of today’s traditional #jobs are at risk. With #AI becoming more predominant, how can we ensure that our children and their children can thrive in this future?

How to get started in #AI? Introducing the Thumbtack Framework
Many people want to get started in AI but don't know where to begin. My advice is to start with your position of strength and leverage your background and skills. You don't all have to learn to program machine learning systems but you can use your past experience to position yourself in the future we're AI is going to be dominant. In this video I share a simple framework that will help you do that. I try to provide perspectives in AI and architecture - touching upon different topics. At times, I also help companies clarify their message in these domains. If you'd like to work with me, please send me a note (through my website Thanks for watching my videos and for subscribing.

Andrew Ng on Building a Career in Machine Learning
Title: Break Into AI: A Q&A with Andrew Ng on Building a Career in Machine Learning Date: 12/4/2018 Andrew Ng will share tips and tricks on how to break into AI. He will discuss some of the most valuable skills for today's machine learning engineers, how to gain the experience to successfully switch careers, and how to build a habit of lifelong learning. He will also take questions from aspiring engineers and business professionals who want to work on AI-powered products. SPEAKER Andrew Ng, General Partner, AI Fund; CEO, Landing AI; Adjunct Professor, Stanford University. Dr. Andrew Ng, a globally recognized leader in AI. As the former Chief Scientist at Baidu and the founding lead of Google Brain, he led the AI transformation of two of the world’s leading technology companies. A longtime advocate of accessible education, Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department. MODERATOR Juan Miguel de Joya, UN ITU; ACM Practitioners Board is the in-house consultant for Artificial Intelligence and Emerging Technologies at the United Nations International Telecommunications Union.

How to get internship/job in Machine Learning | AI
Get to know more about what it takes to get an internship/job in Machine learning (Artificial Intelligence). What we discussed: Kaggle, Projects on Github, Working with Deep Learning Researchers, Learning Machine Learning, AI Residentships/Fellowships, and Contributing to Open Source

How To Become an Artificial Intelligence Engineer In 3 Easy Step
got my hands on an AI Engineer, to answer the most commonly asked question that I get – How do I start a career as an AI developer? In this episode, Simon Westerlind will give his thoughts and insights on the subject, and answer that very question for us. So how do you start your AI career? Well, let's have a look at what Simons say (no pun intended).

Machine Learning Engineer Jobs, Resume & Salary | Machine Learning Engineer Salary Report | Edureka
( Machine Learning Engineer Masters Program: ) This video will provide you with detailed knowledge of who is a Machine Learning Engineer, what are the salary trends, the job trends and the correct format of an ML engineer's resume. This video will also provide you with the job descriptions, roles and the skills required to become one successful ML Engineer.

How to Build a Compelling Data Science Portfolio & Resume | Kaggle
Learn how to craft and tailor your Data Science resume to get noticed by Hiring Managers. Learn what to include, what not to include, and how to prioritize what to mention. About Kaggle: is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spin up a Jupyter notebook with a single click. Build with our huge repository of free code and data. Stumped? Ask the friendly Kaggle community for help.

Data Works MD: Loyola Student Showcase - Predicting Outcomes and Profitability
Data Works MD June 2020 Predicting the Profitability of Out-of-Print Comic Books and Graphic Novels Like many collectibles, comic books and graphic novels have a wide price trajectory. While some are worth millions of dollars, most resell for far less than the purchase price. For a comic book business, the earlier you identify which items will rise or fall in price, the more you maximize your profits. The best course of action would be to purchase comics that are destined to appreciate in bulk from a distributor at wholesale prices. To do so, you need a way to identify which comics will appreciate in value. For my Loyola data science capstone project, I built a regression model that predicts how much a comic will appreciate when no longer available from the distributor.

Understanding and Predicting Frederick County Animal Shelter Outcomes According to the American Society for the Prevention of Cruelty to Animals (ASPCA), approximately 6.5 million animals enter animal shelters in the United States each year, roughly half dogs and half cats. Only half of these animals get adopted, and about 25% of them are euthanized every year. Although some animals may be euthanized because they are dangerous, there are many innocent animals that lose their lives because they could not find a home. For my final capstone project in Loyola University Maryland’s data science program, I conducted a thorough data analysis for Frederick County Animal Control (FCAC). I used a combination of data visualization and predictive analytics to help FCAC with their daily operations and potentially improve animal adoption rates. Specifically, I created models aimed at predicting whether or not an animal will be adopted. In this talk, I will walk through the end-to-end process of my project, including data cleaning, exploratory data analysis, predictive modeling, and interpreting the results. Greg first got into data and programming while studying astronomy as an undergrad at Yale University. During his time there he was also a member of the Varsity Heavyweight Crew. After graduation, Greg put astronomy on hold to pursue a career in rowing and for the past 7 years has been coaching rowing at the D1 level at Loyola University Maryland. While at Loyola, Greg has returned back to programming and data through the Master's in Data Science program. Greg will be graduating at the end of August and is actively looking for opportunities in the data science field. Kaitlyn graduated from UMBC in 2016 with a Bachelor’s Degree in Mathematics and recently completed her Master’s in Data Science from Loyola University Maryland. She has been working at Booz Allen Hamilton as a data scientist for the past four years.

How to become a Full-Stack Deep Learning Engineer by Forrest Iandola
Historically, AI has been an algorithm-centric field. However, with the rise of Deep Neural Network (DNN), it is now the case that (1) large-scale data, (2) novel DNN models, and (3) efficient software and hardware infrastructure, are all key to success. The best outcomes often come from teams who understand the "full stack" from low-level hardware for DNNs to high-level applications of DNNs. Full-stack DNN teams are able to make big-picture tradeoffs in the development of data, models, and infrastructure, leading to practical solutions that exhibit unprecedented levels of accuracy, speed, energy-efficiency. In this talk, Forrest Iandola focuses on three main topics. First, he inclusively defines the "full stack" of skills and technologies that go into DNN engineering. Second, he describes a playbook for managers who want to build, coach, and grow a full-stack DNN engineering team. This playbook draws on lessons that Forrest have learned first-hand at UC Berkeley, Microsoft Research, and DeepScale. Finally, he provides advice on how a generalist or specialist engineer can engage with a full-stack DNN engineering team, and describes a path for how to ultimately become a full-stack DNN engineer. Forrest Iandola completed a PhD in Electrical Engineering and Computer Science at UC Berkeley, where his research focused on deep neural networks. His advances in scalable training and efficient implementation of deep neural networks led to the founding of DeepScale, where he is CEO. DeepScale is focused entirely on building perception systems for automated vehicles, and DeepScale has a number of engagements with automakers and automotive suppliers.

How to get into AI + Facebook AI reading list (the best resources)
Hi, I'm Oleksii and I work at Facebook AI Research. The list of the best resources to learn ML and AI is right here.

 ========== Stage 1 ==========

-Theory: Linear Algebra, Statistics, Theory of Probability (any courses/books will work)

-Practice: Basic Python skills, NumPy

 ========== Stage 2 ==========

-Theory: Online courses / books on ML and Deep Learning; Machine Learning course by Andrew Ng, Coursera; Deep Learning Book, Goodfellow; Stanford CS231n course; Recent MIT course:;

-Practice: Pandas, PyTorch, Tensorflow and "official" tutorials (form their websites)

 ========== Stage 3 ==========

-Theory: "Classic" papers, e.g. AlexNet, ResNet, BERT; Reading lists: MILA reading list: Andrej Karpathy's blog:

-Practice: Random tutorials on the internet, Advanced tutorials, replicating papers

 ========== Stage 4 ==========

-Theory: New paper on your topic, conferences, ArXiv, mailing lists and news channels;;

Sebastian Rudder: ; THE BATCH:

-Practice: Working on toy-projects; Contributing to open-source projects; Getting an internship; Trying to get hands-on experience in any way possible

 ========= Never Stop =========

These all will be usefull for anyone, engineer or researcher. Also try to concentrate on one particular topic and get an expert in it first, it will be much easier to accomplish and will open you opportunities right away. Don't try to become an expert in everything and also do not neglect "not prestigious" experience. Any experience is very valuable.

Mental Resilience

Building your "mental resilience" as you head into a world where you will need to continue to reinvent yourself to leverage/compete with artificial intelligence - continually making yourself relevant. To do so, you need to gain data science expertise; again an understanding how best leverage artificial intelligence/machine learning capabilities and applications.

Why the rise of AI makes "mental resilience" so important
Israeli historian Yuval Noah Harari explored the past and future of humanity in his books "Sapiens" and "Homo Deus." They became international bestsellers and were praised by a wide-range of thought leaders, including former President Barack Obama and Bill Gates. In his new book, "21 Lessons for the 21st Century," Harari focuses on the present and dissects the most pressing issues facing humanity. He joins "CBS This Morning" to discuss why it's important to teach children "emotional intelligence" and "mental resilience" as they head into a world where they will need to continue to reinvent themselves to compete with artificial intelligence.