My Journey into AI – learning resources recommended by the speakers

On April 13, DeepLearning.AI and Omdena assembled a panel of machine learning practitioners who shared their first-hand experience of going from non-traditional starting points to building a career in AI. For any aspiring machine learning engineers who missed the event, here’s a few of the speakers’ tips that we hope you’ll find helpful: 

• The most important part of the job is cleaning data, so data science courses will be a good place to start. 
• Try a bit of everything when you’re starting, especially when you’re not sure what to dedicate your time on
• Read a lot of articles on various topics like statistics, data science and NLP on Medium publications.
• It’s great to be a part of Slack workspaces where you can get and provide help 
• If you can’t find mentors, look for co-learners
• Mentoring helps you better your own skills and knowledge base. 
• Match your capabilities with the companies you are looking for
• Keep learning and evolving with the projects you pick up
• Good communication habits are key – learning to communicate well is just as important as technical skills
• I learn the most in the shortest time by being around people who are already doing the work and finding opportunities to collaborate with them 
  

Additionally, the speakers shared a list of learning resources that they found useful: 

1. DeepLearning.AI online courses 
2. Aggregators 
   • Towards Data Science
   • Medium
   • DeepLearning.AIs newsletter The Batch
3. Companies to follow 
   • Google AI
   • Facebook AI 
   • Github
   • Stack Overflow
   • Kaggle Notebooks – The best way to learn implementations from top kagglers and competition winners
4. Blogs/Newsletters/Research paper/Books:
   • Blogs like Omdena to understand interesting implementations and approaches to solve a problem
   • Hands on ML book by Aurélien
   • Two Minute Papers on Youtube summarizes research papers
   • Deep AI , Arxiv – websites and Twitter handle for latest research papers.
   • Papers With Code for the latest datasets and implementations.
   • Future Is Intersectional: talks and amazing resources presented by Spelman College, on topics of intersectionality, race, and ethics in AI/tech
   • Jay Alammar’s illustrated series like this one helps visualize difficult concepts
5. YouTube and Podcast channels:
   • AIEngineering for End to End ML implementations.
   • 3blue1brown for mathematics intuition.
   • What’s AI for the latest advancements in AI to stay up to date with them
   • Computerphile channel on Youtube also helped me understand some parts of NLP
   • Practical AI
6. Communities/Events
   • PIE & AI meetups  by DeepLearning.AI (get to hear other people’s stories and projects, network)
   • Montréal.AI
   • Women Who Code – Data Science: their community has an archive of recordings on topics of ML/NLP 
   • ML Explained – Aggregate Intellect
   • Caleekal AI
7. Influencers to follow
   • Andrew Ng
   • Ian Goodfellow
     
     

Click here to watch the full event recording.

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