Learning AI is extremely important in both the present landscape and for the future for a few key reasons:
1. AI is revolutionizing many industries – From transportation to healthcare to finance, AI is being used to improve efficiency, accuracy, and decision making across sectors. Understanding AI positions you to take advantage of major opportunities.
2. Critical in-demand skill – AI expertise is hugely sought after. According to some reports, there are not enough qualified candidates to fill over 300,000 roles requiring AI skills. Pursing AI can lead to lucrative career paths in the coming decades.
3. Preparing for the future of work – AI will increasingly automate routine tasks and change jobs of the future. Learning AI ensures humans can remain complementary in an AI enhanced workflows rather than being displaced by them. It will be a crucial future-proof skillset.
4. Unlocking innovation – Many innovative companies at the intersection of AI, software and robotics like SpaceX, Neuralink, OpenAI etc will shape the next wave of progress. With AI skills you can become an inventor in those spaces.
5. Personal growth and progress – There is simply something inspirational about understanding a field moving at blistering pace like AI. Applying models to real problems exercises human creativity through a symbiotic human-AI partnership.
In essence, if you want highly paid, world-changing skills for the next 20 years rather than the last 50, prioritize learning AI today. Let me know if any part needs more detail!
Starting to learn AI
Here are some tips for starting to learn AI/machine learning from scratch as a programmer:
Prerequisites:
– Have a solid grasp of programming in at least one language like Python, Java, C/C++ etc. Python is most common for AI.
– Know some statistical concepts – probability, distributions, regression, etc. Math skills are important.
– Have some basic computer science knowledge like data structures and algorithms.
Getting Started:
– Learn the core machine learning concepts and algorithms – start with supervised learning methods like linear regression, logistic regression, neural networks, etc.
– Master the basics of Python data science libraries like NumPy, Pandas, Matplotlib. These are crucial for the practical aspects.
– Learn a machine learning framework like TensorFlow, PyTorch, or scikit-learn. TensorFlow and PyTorch are used for deep learning.
Progressing Further:
– Work on some beginner ML hand-on projects in areas like computer vision or natural language processing to apply skills.
– Learn more advanced neural network architectures like Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), etc.
– Experiment with different hyperparameter tunings and model optimizations.
– Consider specialized disciplines like reinforcement learning, graph neural networks, etc.
In terms of timelines, most recommendations suggest:
– Basics: 4-8 months
– Intermediate Skills: 4-8 more months
– Advanced Productionizing Models: 1+ years of focused work
The key is to continuously build projects with increasing complexity, read papers/books to supplement, and participate in online courses or coding challenges relevant to AI. Be patient and persistent, it is a vast field. Let me know if any part needs more explanation!
Online platforms for learning AI
Here are some of the best online platforms for learning AI, both free and paid:
Free Options:
1) Coursera – Coursera has a lot of excellent beginner AI/ML courses including offerings from Stanford, deeplearning.ai, IBM and more. Certificates are paid but auditing content is free.
2) edX – Similar to Coursera, edX also has programs in AI/ML from institutions like MIT, Columbia, Microsoft and more. You can audit for free.
3) Kaggle – Kaggle offers free introductory AI/ML courses as well as hands-on programming challenges with real datasets and community support. Great for gaining practical experience.
Paid Options:
1) Udacity – Udacity has a very comprehensive AI/ML nanodegree program covering machine learning, deep learning and more advanced optional courses. The projects are hands-on.
2) Udemy – There are a wide variety of AI/ML courses here from beginner levels to advanced topics by expert instructors. Prices range from $10-$100+ per course.
3) DataCamp – Datacamp’s structured courses and hands-on learning platform is very suitable for learning python, data science/AI and gaining practical coding skills through their guided projects.
4) Fast.ai – Fast.ai’s courses take a more application focused “top down” approach starting with practical deep learning first. Better for those with some coding experience already.
In summary, I recommend starting with some of the free offerings to gain fundamentals first. Once you have the basics down, paid offerings provide more extensive content and credentials. Let me know if you need any other recommendations!
YouTube channels for learning about AI/machine learning
Here are some excellent YouTube channels for learning about AI/machine learning:
1. Siraj Raval – Siraj has one of the best AI/ML channels on YouTube. He covers core concepts, neural networks, programming tutorials, projects and more. Good for beginners.
2. Sentdex – Python programming tutorials for data science, machine learning and core AI concepts. Great for learning the practical coding skills.
3. MathWorks – MATLAB & Simulink tutorials on machine learning and deep learning topics. More focused on the underlying math concepts.
4. StatQuest with Josh Starmer – Explanations of machine learning algorithms with visuals and simple analogies by a seasoned statistician. Engaging teaching style.
5. 3Blue1Brown – Not exclusively AI/ML but some absolutely brilliant visual explanations of the math concepts behind neural networks and deep learning.
6. Tensorflow – The official TensorFlow channel has great tutorials introducing core TensorFlow concepts and applications.
7.深度学习导论 – Yann LeCun’s Deep Learning Course in Chinese taught at NYU. Slides are in English. Advanced theoretical content.
8. Welch Labs – Neural networks concepts explained through visualization and applications in finance sector.
I suggest supplementing video content with coding your own projects, reading documentation/books and potentially taking some structured online courses as well. Let me know if you need any other YouTube channel or resource recommendations!
Best AI blogs, websites and communities to learn about and keep up with the latest in AI
Here are some of the best AI blogs, websites and communities to learn about and keep up with the latest in AI:
Blogs & Websites:
– Andrej Karpathy blog – Thoughts on AI and deep learning from Director of AI at Tesla.
– Colah’s Blog – Neural networks visualizations and explanations by Christopher Olah.
– AImachines.in – A Rss Feed AI news, topic related to AI will help you to understand what is going on in the AI industry.
– Analytics Vidhya – Data science and machine learning blog.
– Papers with Code – Summarizes the latest ML papers with associated code.
– DeepMind Research Blog – Latest publications from researchers at DeepMind.
– OpenAI Blog – Engaging updates from the nonprofit AI research company.
– The Gradient – Machine learning publication from Paperspace.
Communities:
– Reddit MachineLearning – Discussion forum for ML practitioners.
– Kaggle Forums – Q&A from Kaggle’s community of data scientists.
– Fast.ai Forums – Deep learning community with beginner-friendly help.
– Stack Exchange (AI/ML sections) – Q&A site professional data scientists.
In addition, I would recommend follows top researchers like Yoshua Bengio, Geoff Hinton, Andrew Ng, Jeremy Howard etc. on Twitter or LinkedIn for most recent updates.
Let me know if you would like any other blog, website, or community recommendations! Joining relevant groups and staying engaged will help learning immensely.
In my opinion, the best way to start learning AI is through an interactive coding-focused online course that strikes a balance between teaching concepts and practical application. This gives you a solid foundation before delving deeper.
Specifically, here is the approach to starting with AI/ML I would recommend:
1. Take an introductory machine learning course like Andrew Ng’s famous course on Coursera or Udacity’s Intro to Machine Learning. This will teach the fundamentals of machine learning algorithms.
2. Take a more extensive and project-focused course like TensorFlow Developer Certificate on TensorFlow.org or Fast.ai Practical Deep Learning for Coders. These will teach to implement models in code.
3. Choose a subdomain area you are interested in like computer vision, NLP or robotics and take specialized courses and work on a few mini-projects to apply skills.
4. Expand your practical experience by participating in machine learning competitions websites like Kaggle or working on open-ended community datasets.
5. Consider a career pivot or role shift into an ML Engineer or Data Scientist role where you can gain professional experience. Continue learning on the job.
The most important things starting out are to a) get exposed to the fundamentals, b) learn by coding as much as possible through courses with projects and c) continuously apply your skills to build intuition. Let me know if you need any other tips!
