How to learn about ML/AI if you don’t have tech skills

Art by AI

I’m a liberal arts grad. I love words and language. I teach soft skills. Qualitative data is my jam. I’m also obsessed with machine learning (ML) and artificial intelligence (AI).

In 2015 I tumbled down the AI rabbit hole after discovering a long read on the fabulous site Wait But Why. The site explains complex ideas paired with hilarious stick figures. The two part series on AI, The Artificial Intelligence Revolution, was my gateway article to the world of AI, and later ML as part of AI.

So far my self-directed learning journey has only included reading about AI and writing about its affect on hiring and the future of work. I can’t code in Python (with zero plans to do anything with R). My data background includes data analytics, cleaning data, and putting it into Tableau but nothing close to data scientist. I also have no interest in going that far professionally. As a non-tech person trying to access ML/AI, it’s been a challenge to figure out where I fit in. I’ve uncharacteristically avoided meetup groups or conferences on the subject since I don’t have the tech skills.

Not me.

Last month I changed that. I got tired of reading. I wanted idea exchanges. So I attended a ML/Al unconference in PDX. And hot damn I found my people!

An unconference is the opposite of the standard conference setup. Instead of corporate-sponsored keynotes paired with bland chicken and an abundance of shy speakers who read PowerPoints, the participants chose the content. We pitched and voted on what they wanted to talk about. The result was facilitated conversations about subjects we were curious about and a format that flowed. It was the ideal setup for idea exchange and learning. If you’re conference weary an unconference will restore your faith in professional development.

Many people at the unconference were data scientists or computer scientists, and some working on ML projects. A few were students or job seekers. I met one other person who is like me, a communications expert without a technical background who works for a machine learning platform, BigML (and they’re doing rad stuff).

In our sessions we covered a roving range of topics about ML/AI: novel data sets, making AI more accessible to the masses, establishing trust with users, data security, AI decision making re: self-driving cars and the Arizona accident, becoming a data scientist and machine learning engineer, the future of companies and jobs (my pitch!), learning ML/AI as a new person (do you learn the math, the code, or find a project first? plenty of debate on this!), and plenty more side conversations that spilled out of the main sessions.

After years of consuming content about ML/AI I was thrilled to have in-depth discussions about it. (sidenote: the majority of my professional network and peer network are not tech people – their eyes glaze over a bit when I start talking about it.)

As an non-tech outsider it’s a bit intimidating to participate in such a cutting-edge tech space. I think ML/AI people forget that at times. One of the guys I met at the conference noted that when you’re an expert it’s hard to remember how hard it is for others to start in your field. I’ll add that this goes double if you’re in a quant and code heavy field like machine learning. Luckily most everyone at the unconference made it easy to participate (as did the unconference format).

My main takeaway though is that you don’t need to be a software engineer, data science expert, or code wizard to understand ML/AI.

So for all the people who are curious about ML/AI but don’t know how to start engaging in these communities, here’s how. 

Learn the basics: Know the difference between machine learning and AI; understand the difference between Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence; understand the basics of data science. There are no shortage of intro articles and videos on the subject (two examples below).

Here’s a helpful Quora answer about the differences between a data scientist and a machine learning engineer. 

Prior to the uconference I was slightly worried I’d be left out of the conversation if it turned to technical. I prepared by returning to a set a YouTube videos I’d skimmed a while back: Fun and Easy Machine Learning. The YouTube list animates over 15 models to better understand machine learning.

Ignore the math and coding right now: Unless you want to become a data scientist or machine learning engineer, ignore it. You don’t need it to understand the basics or to explore products or impacts of ML/AI. For example, the Fun and Easy Machine Learning series sometimes dives into the math behind the models. Treat it as you would a foreign language; when you don’t the meaning keep moving forward and focus on what you do understand. Fill in the blanks later.

Read everything about ML/AI in the area you’re interested in. ML/AI for non tech people is a huge field. So narrow it down. Start with general articles about artificial intelligence and learn about it’s expected impact. The World Economic Forum has good articles with a global perspective. For business impacts, check out this history of ML/AI technology by industry/verticals. Then head over to CB Insights to study ML/AI companies (and subscribe to their newsletter as they’re cutting edge everything). Then pick an industry that interests you. Either one that you work in or one that you want to work in. Read everything you can about how machine learning is affecting that industry (it’s affecting all of them – right now finance, healthcare, and insurance are some of the industries talked about the most.) Explore products and platforms in that industry that use ML/AI. Read case studies. I study the future of work. So I read everything I can about ML/AI and it’s affect on workers and organizations: McKinsey, AXIOS, MIT, plus I play with HR Tech.

Avoid the hype. It’s easy to get caught up in the shiny promised of AI. Instead, pay attention to counter narratives, often published outside of the tech reporting ecosystem. Find the counter narrative about AI in your field. I read the amazing research and work by Audrey Waters at Hack Education for a counter narrative to AI edtech hype. Explore bias in ML/AI. Understand how AI isn’t neutral and that gender and race bias is coded into AI systems. Weapons of Math Destruction is an excellent book (and 99% Design has a good podcast on it). We need diverse perspectives and people in ML/AI fields to fight these bias, and non-technical people are part of that fight. 

Go to an event and talk to people: This is the intimidating part. But get over it, embrace the awkwardness, and commit to asking curious questions. Remind yourself of the things that you know. Write down the things that you want to learn. Talk to people until you get the answers to your questions. Ask people how they got into their work, what impact they’re having, and how they’d explain their work to a non tech person. Tell them you’re curious. Some people will just talk at you. Others will teach you. Keep in touch with the people who teach you and simply move on from the ones who talk at you.

Get a project: This builds on not worrying about the math and coding. Instead, get a project. What problem do you want to solve? What problem does your organization need to solve? What data is available? What data is missing? How could ML/AI solve your problem? Starting there will help you lead you in the right direction. You might not have an answer right away. That’s ok. It make take a while to solve it. But that’s the point. You’re learning. Ambiguity is part of the process. So ask around your workplace. Visit the data science or computer science team in your organization (assuming you have one). Find a data scientist in your network or at ML/AI events and ask them how they’d solve your problem. Ask them to break it down. Ask a computer science student what they think.

Start with curiosity, ignore the part about not having a technical background, and see where it takes you.

The Future of Work from an L&D perspective

As stewards of your company’s value, you need to understand how to get your people ready—not because it’s a nice thing to do but because the competitive advantage of early adopters of advanced algorithms and robotics will rapidly diminish. Simply put, companies will differentiate themselves not just by having the tools but by how their people interact with those tools and make the complex decisions that they must make in the course of doing their work. The greater the use of information-rich tools, the more important the decisions are that are still made by people. That, in turn, increases the importance of continuous learning. Workers, managers, and executives need to keep up with the machines and be able to interpret their results. – Putting Lifelong Learning on the Agenda,McKinsey Insights

Here’s a company that’s living that advice:

“The future of learning sabbaticals at Buffer is closely tied with our desire to help create the future of work. There’s a quote from Stephanie Ricci, head of learning at AXA that’s really powerful in explaining how much impact learning will have for employees in the future:

“By 2020, the core skills required by jobs are not on the radar today, hence we need to rethink the development of skills, with 50% of our jobs requiring significant change in terms of skillset”

That is a huge amount of jobs that will require new skills and for organizations and workers that means a lot of learning and developing.”

Why this company implemented a learning sabbatical for its employees, FastCo

Treehouse masters career storytelling

I just flaked out on another Coursera course. I thought this would be the time I stuck with it; I even paid for it in hopes I wouldn’t flake. But flake I did.

I’m still focused on upskilling, so I joined another online school, Treehouse. I’ve used them before to learn html and css basics. I love their UX and the entire feel of their learning experience. I’m surprised that feel matters so much to me – but then again learning environments matter offline, so why shouldn’t it matter online?

So I’m onto a new online learning platform, this time focusing on skills that I need right now. I’m taking their WordPress track as all my websites are hosted on WordPress. I can cobble together awesome themes pretty well but I have no idea how WordPress actually works and Treehouse has a robust track that dives into everything I need to know.

As I was pursuing courses I noticed Treehouse excels in another area: storytelling. More specifically, telling the stories of successful career changers. Making a shift to a new career is a daunting task: you have to obtain the skills and convince employers that you can do the job, the latter of which can be even harder than acquiring the new skills. Career changers struggle with doubt, lack of self-confidence, opaque career paths, and lack of knowledge about hiring companies and opportunities. Treehouse uses profiles to share stories from a wide range of people – former customer service specialists, laid off professionals, personal trainers, urban planners – from across the globe. Seeing diverse stories of successful career changers helps learners visualize themselves doing the same. It’s even possible it gives them a bit more confidence. As they read, they’re likely telling themselves, hey, if they can do it, I can do it too. 

Testimonials about impact are important for prospective online students. But the full stories that dive into the learning journey and offer advice serve a purpose too: to motivate career changers. Treehouse puts out a clear message to career changers: everyone’s doing it and you can to.

So bravo to Treehouse. Here’s hoping other online schools invest the time in career storytelling too.

I received a MOOC certificate and all I got was this lame email

Coursera (and other online learning platforms) push hard to get users to pursue a certificate. I’ve flaked out of plenty of MOOCs in the past with the certificate option completely off my radar. This time I enrolled in the Interaction Design Specialty on Coursera a paying subscriber, so I automatically received the certificate.

I completed my first course, Human-Centered Design: an Introduction, and received my certificate announcement via email. The email arrived paired with suggestions on how I can take advantage of my certificate. The suggestions were terribly underwhelming. The only concrete advice beyond viewing my grade: Add it to your LinkedIn profile.

This is precisely where Coursera misses the boat on helping users connect their learning to career success. Some users may know exactly how to talk to their bosses or future employers about the skills they’ve learned or mastered. But in my experience with career changers and even mid-career professionals who are positioning themselves for promotion, most people don’t know how to talk about their new skills or successes. They don’t know how to position themselves or create a story about their new accomplishments.

There are several opportunities here where Coursera can make a difference. A few of note:

  • Give guidance or language on how to talk to employers about your certificate and new skills
  • Show a video interview with a recruiter who talks about the value of these skills, how they’re applied in the workplace, and so on
  • Share a list of employers that value this qualification or link it to entry level jobs in this field
  • Offer video interviews of successful Coursera students who used their certificate to get a job or promotion

Imagine if Coursera did this early on in the Specialization to get users excited about new career opportunities and motivated to complete the course. Showing users how employers view these skills could help learners develop a framework for talking about their new skills as they learn them. Coursera could add value to the learning experience by helping users understand their future career opportunities.

P.S. With 200+ mil in funding, you’d think Coursera would be able to hire a few designers to snazz up that congratulatory email. I’d love a little more flare to pair with that congrats.

My Coursera specialization experience summed up in 7 tweets.

I came. I tried. I tweeted.

These Twitter reflections read like a stream of complaints. And in a way they are. But they serve another purpose: reminding me about the challenges around creating online learning experiences that are engaging and motivating.

I’m building online courses for students. Right now they’re on-demand and asynchronous. In the future I aim to move to a hybrid model. I’m constantly thinking about how we improve engagement in online learning.

A note on forums: Courses rely on forums as their interactive element. While there is interaction (in some forums), the experience isn’t enjoyable. In fact, it’s often more work. When I get stuck, I have to search for the correct forum and skim through elements to find my answer. And not all courses have active forums, as evidenced by the last tweet. This Coursera course had run before so most responses, if there were any, were old.



Confessional: I’m a half-ass MOOC student

Self-reflection notes on completing my first two courses in the Coursera Interaction Design Specialization

I waited until the last minute each Sunday to complete my homework. Now I know what it feels like when I tell students to complete their job applications before they are due.

I didn’t watch all the videos. A friend told me it’s easier to just skim the text below for key concepts needed to complete the homework. It was.

Sometimes I took the time to review other students work, a requirement to get my own homework graded. Other times I just clicked through. There is so much ambiguity in the grading process, especially across cultures, that I didn’t put much stock into reviewing others work. When I did, the ideas and homework were interesting. But without a way to talk to students about those ideas and ask questions, I quickly lost interest in the review activity. I felt bad, but not too bad – I have no connection to this community or class due to the distance effect and lack of community building in Coursera classes.

Even when I pay for the specialization, sign up for a skill that I’m passionate about learning more (user experience research), I still struggle to get the work done. Despite being obsessed with online learning and building courses for students, I struggle to complete online courses. I don’t enjoy the experience. The lack of connection to the professor, students, and learning environment leave me deeply unmotivated.

I am a half-ass MOOC student.