Top resources to get started in ML - Part 2
Resources for Building Strong Practical Foundations with Josep Ferrer
Hi everyone! It’s Kartik here! Today, I’m partnering with Josep Ferrer from DataBites to explore practical resources for anyone starting out or looking to refresh their Machine Learning skills!
In Part 1, we explored essential theoretical foundations for machine learning, including our favorite online courses, must-read books, and key concepts. These resources provide a solid knowledge base to prepare you for advanced learning, so don’t forget to check it out.
Now, in Part 2 of our Machine Learning resources guide, we'll shift from theory to practice. We'll share tutorials, project-based courses, and advanced resources for NLP and Computer Vision. Whether just starting out in the ML field or exploring NLP, This list of resources will equip you to build your skills and your portfolio.
Get ready to turn your theoretical understanding into practical expertise and advance your machine learning journey.
Practical Courses / Tutorials
Practical courses and tutorials help you get started with a hands-on approach, making learning fun and effective!
General courses
AWS Machine Learning Engineer Nanodegree by Udacity
This nanodegree from Udacity teaches practical machine learning skills using Amazon SageMaker, focusing on building, training, and deploying ML models.Tutorial for Movie Recommender System
Google's Codelabs offers a hands-on tutorial for building a movie recommender system using TensorFlow Recommenders and Flutter, providing a practical application of ML techniques.
Courses about NLP
NLP is one of the fastest-growing fields in AI, especially with the surge of LLMs and AI Chatbots.
NLP with Python by Udemy
This course offers a comprehensive introduction to Natural Language Processing using Python, covering essential techniques and libraries.Practical NLP Introduction by Hugging Face
Hugging Face's course provides a practical introduction to NLP, emphasizing real-world applications with state-of-the-art tools like Transformers.Introduction to LLM on GitHub by Maxime Labonne
This GitHub repository introduces Large Language Models (LLMs), offering insights into their architecture and practical implementation.
Courses about Computer Vision
Computer Vision Nanodegree by Udacity
Udacity’s nanodegree provides a thorough introduction to computer vision, covering essential concepts and tools for image and video analysis.Python and OpenCV for Computer Vision by Udemy
This Udemy course offers a quick start guide to computer vision using Python and OpenCV, focusing on practical implementations and real-world projects.
Classic projects
Start with easy-to-follow projects to understand the theory and concepts you learned in Part 1 better.
Regression:
House Price Prediction Using Linear Regression, LassoCV, ElasticNet, RidgeCV, and XGBoost.
Video Game Sales Prediction
Item Price Prediction
Classification:
Sentiment Analysis
Music Genre Classification
Article Classification
Image Classification using Deep Learning
Clustering:
Document Topic Clustering
Ranking
Movie Recommendation System
Music Recommendation System
Some Tips:
Data Collection and Preparation: Spend time on collecting and cleaning your data. This is often the most time-consuming part.
Model Selection: Evaluate different types of models for the same problem and understand the differences. For example, you can solve the same Article classification using both clustering and classification.
Visualization: Visualize your data and results to gain better insights and communicate your findings effectively. Use tools like Matplotlib and seaborn for that.
Documentation: Document your process, code, and findings thoroughly. This helps in the last step which is:
Share It with the World!
Let people know you got your hands dirty and start building your portfolio.
How to Build Your Portfolio
Write Articles/Blogs: Summarize your learnings and visualize your results on publications like Medium and Substack.
Examples: House Prediction, Twitter sentiment analysis
GitHub / Kaggle: Put your projects on GitHub and Kaggle. Examples:
Examples: Music Recommender, Email Clusterig
Unconventional Ways to build portfolio
People often work on these courses and projects outside of their daily job, but sometimes it's easiest to integrate learning with daily tasks.
For Students
Partner with an advisor in your lab or reach out to professors and PhD students for collaboration.
For Professionals
Join an ML team, work on projects on the side, and dedicate 20% of your working time to learning.
Get a mentor who specializes in ML, either from your company.
Final Conclusion
You might hear that working on offline projects is not useful, but this is only partially true. If you solve projects by just referring to other’s solutions, it won't help with building your portfolio. The key is understanding and demonstrating your learnings. Showcase how you advanced from simple models to complex ones, compare and visualize results, and discuss the pros and cons of different techniques. Doing this for a few advanced projects can significantly strengthen your profile.
You don't need to complete all the resources ofcourse.
These are high-quality resources, and following just a few will be enough to build a strong foundation.
Once you start working on projects, you'll often have questions to ask and things to share. There are numerous online ML communities built specifically to help you:
Reddit Communities: r/learnmachinelearning, r/datascience, r/MLQuestions, r/machinelearning: Supportive communities for beginners to ask questions and share learning resources.
GitHub: It has numerous repositories for sharing and collaborating on machine learning projects and code.
Kaggle Discussions Boards: Forums for discussing Kaggle competitions
Discord:
LearningAITogether : A collaborative space for learning and discussing AI and machine learning topics.
Hugging Face Discord: A vibrant community for discussing NLP and the latest in machine learning with Hugging Face tools and models.
LinkedIn: Professional groups for networking and sharing insights in machine learning and data science.
With these practical resources and tips, you're well-equipped to transform your machine learning knowledge into a solid portfolio.
P.S. Don’t forget to subscribe DataBites and Josep Ferrer. Josep is an expert technical writer in ML with more than 50+ articles in KDnuggets and TowardsDataScience.
Dive in and as always happy learning!
🎉 Good Reads for the weekends
ML
Part 1 of this series by Josep Ferrer and me.
I Spent a Week Diving Into LLMs (for the First Time) as a Data Scientist by Andres Vourakis
OpenAI's SearchGPT and the Impossible Promises of AI by Michael Spencer
Career Development
How to impress in high-stakes presentation by Akash Mukherjee
Making of a Million Dollar Staff Engineer by Gourav Khanijoe and Raviraj Achar
From Staff Engineer at Meta to Y-Combinator Founder by Hemant Pandey
 | A guest post by
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Thank you for this wonderful Resources
Loved the "Classic Projects" and the project list in there. I wish someone had told me this when I was starting. Great work, Kartik and Josep!