Many are drawn to the exciting field of Machine Learning (ML) today. However, the traditional, bottom-up curriculum often proposed by educators can be overwhelming and discouraging for newcomers.
This guide flips the conventional approach, outlining what we believe is the quickest and most accessible path to gaining a solid understanding of ML. We’ll explore a practical, top-down learning strategy designed to get you hands-on with machine learning faster.
Table of Contents
This curriculum follows a looping, multi-step process, designed for iterative learning and continuous improvement:
- Immerse Yourself in the Machine Learning Field
- Study One Project that Looks Like Your Endgame
- Learn the Programming Language
- Learn the Libraries from Top to Bottom
- Do One Project You’re Passionate About in Max One Month
- Identify One Gap in Your Knowledge and Learn About it
- Repeat Steps 0 to 5
This is a cyclical learning plan because step 7 leads you back to Step 1, encouraging continuous learning and refinement.
It’s worth noting that this curriculum might seem unconventional. However, it has been rigorously tested and proven effective when teaching machine learning to undergraduates at McGill University. Through various iterations, starting with the theoretically sound bottom-up approach, experience has shown that this pragmatic, top-down method yields the best learning outcomes.
A common concern is that learners who don’t start with foundational knowledge like statistics and linear algebra will lack a deep understanding of machine learning and struggle with model building. While theoretically valid, practical experience contradicts this.
In reality, students who first grasp high-level modeling are more motivated to explore the underlying theory as they recognize its direct benefits to their practical skills. This context, crucial for deeper learning, is often missing when starting from the bottom up – a pitfall that causes many students to lose interest.
With that said, let’s dive into the learning plan and discover how to effectively learn machine learning! 🚀🚀🚀
Step 0: Immerse Yourself in the Machine Learning Field
The crucial first step in learning any new domain is to invest time in understanding the landscape and identifying your specific interests within it. This initial immersion offers two key advantages:
- Understanding the Scope: Knowing the breadth of the field allows you to recognize what’s encompassed and what might be outside your immediate focus, enhancing your concentration.
- Creating a Mental Roadmap: Familiarity with the overall landscape helps you chart a more effective learning path, similar to understanding a map before a journey.
Imagine that you are a sheep in a pasture. It’s important that you know where the boundaries are and where the grass taste better (Photo by ARANYA KAR / Unsplash).
To effectively immerse yourself and refine your learning plan, systematically address these three questions:
- What can you do with Machine Learning? Explore the vast possibilities and applications of ML.
- What do you want to do with Machine Learning? Identify your specific interests and goals within the field.
- How do you do that specific thing? Investigate the practical steps and tools required to achieve your chosen ML objectives.
These questions guide you towards a focused and manageable learning path while providing a comprehensive view of the broader machine learning landscape.
Let’s examine each of these questions in more detail to build a solid foundation for your machine learning journey.
What can you do with Machine Learning?
This question is intentionally broad, as the capabilities of machine learning are constantly expanding. This iterative curriculum encourages you to revisit this question with each cycle, keeping you updated on the evolving possibilities within the field.
This continuous exploration helps refine your mental model of machine learning. Don’t worry if your initial understanding isn’t perfect; a general grasp is more valuable than no understanding at all.
Think of this question as climbing a foggy mountain and taking note of the landscape down below: (Photo by Lucas Clara / Unsplash)
Here’s a concise overview of machine learning capabilities, spanning technical concepts to practical applications to give you a sense of the breadth of the field.
Technical Machine Learning Topics
- Supervised Learning: This involves training a model with input data and corresponding labeled outputs. Once trained, the model should predict the correct output for new, unseen inputs. Example: Image classification, spam detection.
- Unsupervised Learning: This type uses input data without labeled outputs, tasking the model to discover patterns and structures within the data. Example: Customer segmentation, anomaly detection.
- Reinforcement Learning: This setup involves an agent, an environment, possible actions, and rewards. The agent learns to take actions that maximize rewards over time, similar to training a dog with treats. Example: Game playing AI, robotics.
- Online Learning: This can be supervised or unsupervised, but the key feature is the model’s ability to update itself continuously as new data streams in. Example: Real-time fraud detection, personalized recommendations.
- Transfer Learning: This technique leverages pre-trained models as a starting point for new, related tasks. It significantly accelerates learning for the new task by using knowledge gained from a previous one. Example: Using a model trained on a large image dataset to classify medical images.
- Ensemble Learning: This combines multiple trained predictors (either sequentially or through voting) to create a more robust and accurate final predictor. Example: Random Forests, Gradient Boosting.
While numerous other machine learning approaches exist, these provide a strong foundation for understanding the field.
Common Machine Learning Models
One of the most complicated types of model called a Deep Neural Network.
- Linear Regression: The classic y = ax + b formula, surprisingly effective for many problems and a good starting point for analysis. Use case: Predicting house prices based on size.
- Logistic Regression: Despite “regression” in the name, this model is used for classification, predicting the probability of belonging to one or more classes. Use case: Email spam detection (spam or not spam).
- Decision Tree: Creates a tree-like structure of decisions or rules that lead to a predicted outcome. Valuable for their interpretability and ease of inspection. Use case: Loan approval prediction.
- Support Vector Machine (SVM): Aims to find an optimal hyperplane that maximally separates different classes. Imagine a line with thickness separating two groups. Use case: Image classification.
- Naive Bayes: Applies Bayes’ Theorem, assuming feature independence (often “naive” in practice, but still surprisingly effective). Use case: Text classification, sentiment analysis.
- k-Nearest Neighbors (k-NN): A non-training based classifier that memorizes the dataset and classifies new points based on the majority class among its k-nearest neighbors in the data. Use case: Recommendation systems.
- K-Means: An unsupervised clustering algorithm that groups data points into k clusters based on their proximity to cluster centroids, iteratively refining centroid positions. Use case: Customer segmentation.
- Random Forest: An ensemble method using numerous simple decision trees. The final prediction is based on the majority vote of the trees. Use case: High-accuracy classification and regression tasks.
- Dimensionality Reduction Algorithms: Techniques like Principal Component Analysis (PCA) that reduce the number of features (dimensions) in a dataset while preserving essential information. Useful for visualization and simplifying models. Use case: Feature extraction, data visualization.
- XGBoost (Extreme Gradient Boosting): A highly efficient and regularized gradient boosting algorithm, often a top performer in machine learning competitions. Builds weak learners sequentially to create a strong predictor. Use case: Complex prediction tasks, Kaggle competitions.
- Deep Neural Networks: A vast field of models composed of interconnected layers of nodes, capable of learning hierarchical representations of data. Highly powerful but can be complex to train. Architectures include CNNs (Convolutional Neural Networks) and Transformers. Use case: Image recognition, natural language processing, complex pattern recognition.
While many more ML models exist, mastering Linear Regression, SVM, XGBoost, and a form of Deep Neural Network equips you for a wide range of problems. Understanding how these models learn provides valuable mental flexibility and diverse problem-solving approaches.
Common Applications of Machine Learning
The applications of machine learning are rapidly expanding across virtually every sector where data is generated and analyzed. The breadth and depth of ML applications are constantly growing, so a comprehensive understanding is less critical than grasping the general possibilities.
Type of ML application called image segmentation (useful for self-driven cars).
- Computer Vision: ML, particularly Deep Learning, excels at image-related tasks like object recognition and image analysis. Generative models can even create new images (GANs, Neural Style Transfer). Applications: Self-driving cars, medical image analysis, facial recognition.
- Natural Language Processing (NLP): Encompasses tasks like question answering, language translation, text classification, and text generation. Applications: Chatbots, machine translation, sentiment analysis, content generation.
- Medical Diagnosis: ML aids in analyzing medical images and other patient data (e.g., hormone levels) to assist in diagnosis. Applications: Cancer detection, disease prediction, personalized medicine.
- Bioinformatics: A broad field using ML for analyzing biological data, including protein folding, genetic analysis, and drug discovery. Applications: Drug development, personalized genomics, understanding biological systems.
- Outlier Detection: Identifying data points that deviate significantly from the norm, crucial in various fields for anomaly detection. Applications: Fraud detection, equipment failure prediction, quality control.
- Weather Forecasting: ML excels at analyzing massive temporal datasets, making it valuable for weather prediction and climate modeling. Applications: Improved weather forecasts, climate change analysis.
This list is just a starting point. The key takeaway is that ML’s applicability is vast and growing. Gaining a general sense of these possibilities is crucial for the next phase of your learning journey.
What do you want to do with Machine Learning?
This is arguably the most critical question. It’s impossible to master every aspect of machine learning (or any field). Selective focus is essential to make efficient use of your learning time.
A practical approach is to rank your interests within machine learning in descending order.
Take a good old pen and paper and rank these learning topics 👺: Photo by Adolfo Félix / Unsplash
Choose your top interest and keep it visible as your primary learning focus. Commit to learning this specific area, and defer other interests until your priorities evolve.
Remember, your interests can change. If a topic becomes less appealing after initial exploration, it’s perfectly acceptable to switch focus. This initial planning step is designed for this flexibility.
Even if multiple subjects pique your interest, commit to one per learning cycle. Deeper exploration within a single area reveals interconnections that superficial topic-hopping misses. Concentrating your efforts allows for a richer and more integrated understanding.
For example, in my own ongoing learning journey, a current focus is Graph Neural Networks and their application in Supply Chain Management.
How do you do that specific thing?
Once you’ve identified your interests and their place within the broader ML context, dedicate time to understanding how experts apply ML in your chosen area.
What are they using, what is their setup? Photo by Israel Andrade / Unsplash
Understanding the tools, techniques, and workflows used in your area of interest is crucial. This context grounds your learning and helps you prioritize what to learn and what to skip, saving significant time and effort.
It also helps you identify what not to focus on. For instance, if your research reveals that HTML, CSS, and JavaScript are not central to your chosen ML application, you can de-prioritize learning these technologies.
The tools used in ML are diverse, ranging from programming languages to specialized libraries, depending on the application. Languages include C++, Java, Lua, Swift, JavaScript, Python, R, Julia, MATLAB, Rust, and more.
However, Python and its extensive ecosystem of libraries dominate the field. Python’s ease of learning and rich ecosystem make it a popular choice for ML tool development. Many ML tools are built with Python interfaces, even if their core is implemented in faster languages like C++.
If this technical detail is unclear, the key takeaway is that learning Python and its ML-related libraries is a safe and highly practical bet.
Tools to Use for Machine Learning
The commonly used tools for machine learning include:
- Python: The dominant programming language for ML due to its ease of use and extensive libraries.
- Pandas: A powerful Python library for data manipulation and analysis, essential for data preprocessing and exploration.
- Scikit-learn: A comprehensive Python library providing a wide range of ML algorithms and tools for model building, evaluation, and selection.
- TensorFlow & PyTorch: Leading Deep Learning frameworks for building and training neural networks. PyTorch is known for its flexibility and user-friendliness, while TensorFlow is widely adopted in industry.
- Keras: A high-level API that can run on top of TensorFlow or other backends, simplifying neural network development.
- NumPy: Fundamental library for numerical computing in Python, providing support for arrays and mathematical operations, crucial for ML tasks.
- Jupyter Notebooks/ Google Colab: Interactive environments for coding, data visualization, and documentation, ideal for learning and experimenting with ML.
- Git: Version control system for managing code, essential for collaborative projects and tracking changes.
Many other tools exist, a vast array is listed here! Awareness of these is beneficial, but don’t feel pressured to master every new library. The tools listed above are sufficient for most ML projects, especially when starting out.
However, specialized libraries might be necessary depending on your specific domain of study.
For instance, with my focus on Graph Neural Networks and Supply Chain Management, the standard stack of tools is a good starting point. However, PyTorch Geometric (Pytorch geometric) is a specialized library within PyTorch that significantly accelerates Graph Neural Network development.
Therefore, my tailored tool stack would be:
Python + Pandas + Pytorch + Pytorch geometric + Git + Colab
This selection is based on understanding the tools commonly used by experts in my specific subfield.
Step 1: Study One Project that Looks Like Your Endgame
Having a clear understanding of your goals and the tools involved, the next step is to dive into a real-world machine learning project that aligns with your aspirations.
The most effective way to learn practical skills is to observe and analyze the work of experts. Think of this as an “asynchronous apprenticeship.”
You and your mentor figuring out what all the blinking lights are for: Photo by ThisisEngineering RAEng / Unsplash
Seeing a finished project that represents your target skill level provides invaluable context, far exceeding theoretical learning.
To achieve this, explore public projects on platforms like GitHub or Kaggle. Browse until you find a project that resonates with your interests. This could be a comprehensive library, a focused analysis, or a production-ready AI application. Select a few that appeal to you, then choose the one that excites you most.
Once you’ve chosen a project, spend time reviewing its documentation, codebase structure, and code. Initially, you’ll likely feel lost, especially if you’re new to coding. Embrace this feeling – it’s a sign of learning!
Take notes on recurring patterns, parts you understand, and areas that are unclear. Bookmark this project and revisit it as you progress in your learning.
A good starting point is this GitHub list. Alternatively, search Kaggle (Kaggle) or GitHub with keywords related to your ML interests.
For my own learning plan, a suitable project is Thomas Kipf’s simple Graph Convolutional Network (GCN) implementation. Its simplicity allows for a clear walkthrough and understanding of each section, while also illustrating fundamental structural principles.
Step 2: Learn the Programming Language
With a clear vision of your destination and the necessary skills, it’s time to learn the programming language used in your chosen project.
Python is the most probable language, but depending on your project and interests, you might encounter Julia, C++, Java, or others.
Regardless of the language, invest time in learning the basics to write functional scripts.
Excellent resources for learning Python include freeCodeCamp’s Scientific Computing with Python course or Kaggle’s concise Python course.
Highly recommend this one from freeCodeCamp!
Complete language mastery isn’t necessary initially. Iteratively refine your language skills with each pass through this curriculum.
For my learning plan, the freeCodeCamp course provides sufficient Python knowledge to get started.
Step 3: Learn the Libraries from Top to Bottom
Many machine learning curricula begin by implementing algorithms from scratch after covering the basics.
While valuable as a standalone exercise, this shouldn’t be the primary focus early in your ML learning journey.
Implementing algorithms from scratch is rarely done in practice, except by library developers. Even then, they often rely on lower-level linear algebra libraries.
While understanding the inner workings is beneficial, it’s not an immediate priority.
Instead, focus on learning the highest-level libraries in your chosen language that enable you to achieve results quickly. Learn to use these high-level packages effectively enough to create functional applications.
Initially, you’ll lack a deep understanding of why things work, but this is acceptable at this stage.
The priority is to gain hands-on experience with the tools used by ML experts daily. Once you grasp the high-level library’s functionality, gradually explore lower-level libraries for deeper understanding.
Avoid going too deep too soon. Getting bogged down in low-level details (like LAPACK or Fortran) prematurely can be demotivating.
For my project, the main library is PyTorch, or a higher-level wrapper like Fast.ai. A practical course like fast.ai would be ideal.
Step 4: Do One Project You’re Passionate About in Max One Month
This is where the most significant learning occurs. By this point, you should have enough foundational knowledge to assemble a minimally functional project.
If you feel completely confident at this stage, you likely haven’t progressed rapidly enough through steps 0-3.
Choose a project within your area of interest that you genuinely want to create and develop. Keep the project scope manageable, aiming for completion within 1 week to 1 month maximum.
Mark a deadline in your calendar with reminders. A time-bound project provides motivation and healthy pressure to ensure completion.
The goal is to struggle just enough on a small project to identify knowledge gaps and experience the realities of an ML developer’s workflow.
Working independently, without structured course guidance, you’ll tackle the challenging aspects of ML projects:
- Planning, scoping, and tracking project progress.
- Navigating online documentation for libraries.
- Troubleshooting using StackOverflow, GitHub threads, blogs, and forums.
- Iteratively building and improving your project.
- Debugging overfitting, underfitting, and generalization issues.
To select an engaging project:
- Reflect on your current interests.
- Explore lists of project ideas.
- Browse open datasets.
Combining these approaches helps you understand project possibilities and mix-and-match interests to create something unique.
This GitHub list offers project inspiration. Combine it with Google Dataset Search engine to find relevant data.
⚠️ Data availability is crucial. ⚠️
Even excellent project ideas are hindered by lack of data.
For my interests, I found a dataset on a Mining Company’s Global Supply Chain with sufficient data for a project. My project will involve modeling this data as a graph and using Graph Neural Networks to predict excavator sale prices.
Step 5: Identify One Gap in Your Knowledge and Learn About it
Having completed a project, you’ll likely be impressed by your progress, yet also aware of the limitations and challenges encountered.
You’ll realize how much you still need to learn and identify specific areas where your knowledge is lacking.
You rejoicing in your newfound ignorance! Congrats 👏 : Photo by Daniel Joshua / Unsplash
This is a positive sign! Create a list of knowledge gaps encountered during your project, ranked by perceived importance. Initially, everything might seem critical, but prioritizing is a valuable exercise.
Now, and this might seem counterintuitive, focus on only the single most important knowledge gap.
Eliminate all other items from your list, focusing solely on the top priority. In each learning loop, your initial assessment of what’s crucial will be imperfect. Focusing narrowly allows for deeper learning in a key area.
Allocate 1 day to 1 week to learn about this specific topic. This short timeframe encourages focused learning to gain functional knowledge for the next learning cycle.
Study that one little bit of knowledge very hard for a short period of time: Photo by Green Chameleon / Unsplash
In practice, you might discover connections to other important topics like probability, statistics, or linear algebra. Explore these links as they arise, reinforcing your mental model of machine learning.
Step 6: Repeat Steps 0 to 5
Your first pass through this learning pipeline will likely be imperfect. However, you will have learned significantly more, in less time, than with a traditional bottom-up approach.
The value of this method increases with each iteration. Each cycle becomes easier, and your understanding of the field deepens.
This methodology is inspired by lean principles applied successfully at my startup. Iterative refinement is the fastest path to achieving your goals.
Three step lean cycle, image taken from here.
Within a year, you could complete 12 cycles, meaning 12 machine learning projects and a deeply practical understanding of the field.
This method makes you highly employable and equips you with the tools for continuous self-improvement.
Interestingly, this iterative learning process mirrors gradient descent in machine learning. You’re effectively performing gradient descent on the “learning machine learning” problem, taking small steps to reduce your “ignorance cost.”
You’re even using a variant akin to Nesterov-accelerated gradient, anticipating future knowledge needs and adjusting learning pace accordingly.
This is you in your cost plane of machine learning ignorance going waaaaaay down.
Summary and Conclusion
In summary, to learn machine learning effectively:
- Map out the ML field and create a mental framework.
- Find and study an inspiring project that aligns with your goals.
- Learn the necessary programming language.
- Learn essential libraries to build functional applications.
- Complete a personal project within 1 week to 1 month.
- Identify and address one key knowledge gap.
- Repeat the cycle for continuous improvement.
This approach offers a practical, efficient, and engaging way to learn machine learning. Don’t hesitate to connect on LinkedIn if you have feedback on this process. For more in-depth ML topic explorations, check out my YouTube channel.
Happy learning! 👋
Useful Machine Learning Resources
This section provides a curated list of recommended learning resources for aspiring machine learning practitioners. While not exhaustive, it’s a strong starting point for building a solid foundational understanding of Machine Learning.
Machine Learning Books
The Elements of Statistical Learning By Hastie et al.
A foundational text in the ML community, highly recommended for repeated study.
Great introductory book!
Artificial Intelligence, A Modern Approach (3rd Edition)
Provides a comprehensive overview of the broader field of Artificial Intelligence, encompassing Machine Learning within its context.
This book ties the whole AI/ML/DL field together quite nicely
The Deep Learning Book
A classic in Deep Learning, surprisingly accessible with a basic linear algebra background (primer included).
The cover is very neat as it was generated by a Deep Neural Network
Python Data Science Handbook: Essential Tools for Working with Data
Excellent for enhancing your data science skills in Pandas and NumPy, leading to more efficient and readable code.
Keep it close to your desk and do the exercises
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
A top recommendation for getting started quickly. Thorough, practical, and hands-on.
Take either the HD lizard or the grayscale one, both are very solid!
Deep Learning with PyTorch
Valuable insights from PyTorch creators into their philosophy, including neuroscience examples.
The visuals for this one are very well done, especially the tensors part.
Machine Learning Blogs
Machine Learning Mastery
A frequent search result for ML topics, known for well-written content and strong SEO. machinelearningmastery.
Analytics Vidhya
Another common Google search result, offering abundant ML content. analyticsvidhya. While sometimes appearing slightly promotional, it contains valuable information.
Distill
A visually rich and insightful journal for machine learning topics. distill.pub. Currently on hiatus but archives contain high-quality content.
Machine Learning Community
r/MachineLearning
A leading community for latest ML developments and discussions. Great community to stay updated and get diverse perspectives.
Contains valuable learning resources, such as this guide:
This is very blunt, but still relevant.
r/LearnMachineLearning
Excellent community for newcomers to ask questions, share projects, and find inspiration. excellent community for newcomers.
Machine Learning Events
MAIN (Montreal AI and Neuroscience) Conference
A conference bridging Neuroscience and Machine Learning. 2020 link & Youtube video
A great venue to explore advancements in Computational Neuroscience.
NeurIPS (Neural Information Processing Systems) Conference
A major machine learning conference focused on neural networks. mythical machine learning conference. While large and sometimes debated for its current scale, accepted research offers valuable insights.
Many other conferences exist, a lot more out there . Attending conferences provides valuable exposure to cutting-edge research, though it can be intense.
Online Machine Learning Courses
Fast AI
A top recommendation for online courses, embodying a pragmatic, action-oriented approach. this one from FastAI. The instructor is also highly engaging.
Andrew Ng ML (of course)
A highly recommended classic course by Andrew Ng on Machine Learning. Andrew Ng’s course on machine learning.
freeCodeCamp’s Machine Learning and Data Analysis YouTube Courses
freeCodeCamp offers numerous free and excellent Machine Learning and Data Analysis courses on YouTube, including:
freeCodeCamp’s Machine Learning and Data Analysis YouTube Courses
Explore their YouTube channel for a wide range of learning resources.
