Conquer Machine Learning: A Structured Roadmap with Resources and Kaggle Winning Solutions

In the age of Artificial Intelligence (AI) and Generative AI , machine learning (ML) algorithms are everywhere. They power the recommendations you see online, the filters in your favorite photo apps, and even the spam filter in your email. We have a powerful tool at our fingertips, capable of learning from data and making predictions about the future. That’s the essence of Machine Learning (ML). But to truly unlock its potential, there’s a foundational layer you might be wondering about: have you ever wondered if you can use these algorithms without getting bogged down in complex theories and math?

You can technically run machine learning (ML) algorithms without a deep understanding of ML theory, statistical learning, Probability theory and related mathematics, but there are significant drawbacks to this approach. Here’s a breakdown of the pros and cons:

Pros

• Quick Start: You can get started using pre-built tools and libraries that offer pre-configured algorithms with default settings. This can be helpful for simple tasks or initial exploration.
• Accessibility: There are user-friendly interfaces and visual tools and open sources and libraries that allow you to train models without writing complex and low level code.

Cons

• Limited Choices: You might not know which algorithm is best suited for your specific problem and data. Different algorithms have different strengths and weaknesses, and choosing the wrong one can lead to poor results.
• Black Box Effect: You won’t understand how the algorithm works internally or why it makes certain predictions. This makes it difficult to interpret the results, identify potential biases, or troubleshoot issues.
• Difficulties in Tuning: Optimizing the model’s performance through hyperparameter tuning becomes challenging. You might not understand the impact of different parameters on the model’s behavior.
• Limited Generalizability: It’s difficult to assess how well the model will perform on unseen data. You might end up with a model that overfits the training data and doesn’t generalize well to real-world scenarios.

It’s not necessary to be a full-fledged statistician to use ML effectively. However, having a basic understanding of core ML Theory fundamentals, statistical principles, and some related mathematics will significantly enhance your capabilities.

The Roadmap

This roadmap is drawn from my experiences in data science, machine learning journey, and my role as a mentor in the KaggleX BIPOC Mentorship Program cohorts. This plan empowers you to embark on a structured journey into the fascinating world of Machine Learning (ML) theory and fundamentals. It equips you with a strong foundation for further exploration, breaking down the learning path into stages with clear time-frames and recommended resources.

Stage 1: Foundational Mathematics

This stage prioritizes building a solid foundation in mathematics essential for understanding ML algorithms. Here, statistics and probability stand alongside linear algebra and calculus.

A. Statistics and Probability 

Key Concepts: Probability distributions , statistical inference (hypothesis testing, confidence intervals), expectation maximization (EM algorithm), basic concepts of random variables and central limit theorem.

One of the following primary text resources :

• “Math for deep learning”, by Ronald T. Kneusel

• “Introduction to Probability” by Joseph Owen (clear explanations)
• “ A First Course in Probability, 10th edition/Global Edition, 2019”, Sheldon Ross University of Southern California

Advanced (optional) resources : Probabilistic machine learning”: a book series by Kevin Murphy (introduces probability in ML context)

• "Machine Learning: A Probabilistic Perspective" (2012)
• "Probabilistic Machine Learning: An Introduction" (2022)
• "Probabilistic Machine Learning: Advanced Topics" (2023)

B. Linear Algebra 

Key Concepts: Matrices, vectors, linear transformations, eigenvalues & eigenvectors, matrix decomposition , etc.

Resources

• Primary Interactive Course: “Introduction to Linear Algebra” by Gilbert Strang , MIT Open Course (problem sets included)
• Advanced (optional):“Linear Algebra Done Right” by Sheldon Axler (in-depth treatment)

C. Calculus : 

Key Concepts: Differentiation,matrix calculus , optimization techniques (Gradient descent, Jacobians and Hessians , etc ).

Resources:

• Primary Text: “Math for deep learning”, by Ronald T. Kneusel
• Primary Text and widely used textbook: “Calculus” by James Stewart 

Stage 2: Machine Learning Fundamentals

This stage introduces core ML concepts and algorithms.

A. Introduction to Machine Learning (2–4 months)

Become familiar with supervised learning and unsupervised learning, data preparation , common algorithms like linear and logistic regression, tree based algorithms , k-nearest neighbors, Support Vector Machines, validation strategies and metrics with python.

Resources:

• Primary Practical Guide: “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” (with Python code examples) , by Aurélien Géron
• Supplemental Online Course(optional): “Machine Learning Crash Course” by Google: (interactive and accessible)

In this stage, I recommend participating in relevant Kaggle tabular playground competitions. This will allow you to test essential methods with code and gain practical experience. Additionally, you will become more familiar with Python open-source libraries commonly used in machine learning. By reviewing code from previous competitions, you can learn a multitude of techniques for tackling machine learning tasks.

B. Statistical Learning Theory (2–5 months)

Delve Deeper Into bias-variance tradeoff, overfitting, generalization, PAC learning theory, Vapnik-Chervonenkis (VC) dimension.

One of the following primary text resources :

• Primary Theoretical Exploration: “Understanding Machine Learning: From Theory to Algorithms” by Shai Shalev-Shwartz and Shai Ben-David. Possible roadmaps based on the book have presented by authors in the introduction of the book.
• Primary Theoretical Exploration: “Introduction to Statistical Learning, Python Edition, 2023, by :

• Advanced (optional): “Foundations of Machine Learning” by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (in-depth treatment)

C. Optimization (1–2 months)

Focus more on gradient descent and its variants (SGD, Adam, RMSprop), optimization landscapes, convexity, convergence guarantees.

Resources:

• Primary Theoretical Exploration: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Chapter 8: Optimization)
• Advanced (optional): “Convex Optimization” by Stephen Boyd and Lieven Vandenberghe (advanced resource)

Remember, the goal is to be able to use ML algorithms effectively, not just run them blindly. By investing some time in learning the underlying theory and statistical concepts, you’ll unlock a deeper understanding of the ML landscape and achieve better results with your models.

Stage 3: Deepen Your Knowledge (ongoing)

Stage 3 is all about solidifying your understanding of Machine Learning (ML) concepts and venturing into more advanced topics. This is an ongoing process as the field of ML is constantly evolving. Here’s a breakdown of key areas to focus on in this stage:Probabilistic Methods in Machine Learning:

Deepen Core Concepts:

• Strengthen your grasp of fundamental algorithms: This includes revisiting topics like linear models, tree based algorithms , ensemble methods e.g., Random Forests, Gradient Boosting Machines (e.g. XGBoost) with a deeper focus on their theoretical underpinnings, strengths, weaknesses, and hyperparameter tuning strategies along with advanced techniques for anomaly detection , performance boosting techniques like pseudo labeling (semi-supervised) and stacking.
• Delve more into optimization theory: Gain a deeper understanding of optimization algorithms , their convergence properties, and how to address challenges like vanishing/exploding gradients.

Note : As one progresses through these stages, the realization dawns: "The more I learn, the more I discover the vastness of what remains unknown." This realization can ignite a yearning for deeper knowledge. The pursuit of excellence can manifest as a desire to identify the most cutting-edge techniques, develop the most efficient pipelines, and create the most intuitive user experience possible. However, this relentless pursuit of perfection can sometimes lead to feelings of inadequacy, a phenomenon known as imposter syndrome ! Recognizing this phenomenon and its potential impact is crucial to overcoming it.

Deep Learning Fundamentals:

• Introduction to Deep Learning: Grasp the core concepts of artificial neural networks , activation functions and loss functions .
• Deep Learning Frameworks: Choose a framework like TensorFlow, PyTorch, or Keras. Learn its core functionalities for building, training, and evaluating deep learning models.

I recommend after walking trough previous stages continue deep learning journey with tabular data e.g. "Modern Deep Learning for Tabular Data" by Andre Ye Zian Wang .

Deep Learning Architectures

• Convolutional Neural Networks (CNNs): Understand the architecture of CNNs, including convolutional layers, pooling layers, and their applications in computer vision (image classification, object detection).
• Recurrent Neural Networks (RNNs): Explore RNNs (LSTM, GRU) for sequential data like text or time series forecasting. Grasp concepts like backpropagation through time (BPTT).
• Attention Mechanism and Transformers: Understand the architecture and applications of transformers, a powerful architecture for natural language processing (NLP) tasks like machine translation and text summarization.
• Autoencoders and Generative Adversarial Networks (GANs): Explore autoencoders for dimensionality reduction and anomaly detection, and GANs for generating realistic data (images, text).

Specialize in a Domain:

• Identify an area of interest: Consider your background, interests, and potential career goals. Is it computer vision, natural language processing, recommender systems, time-series for different business use cases or something else.
• Focus on domain-specific techniques: Many domains have specialized ML techniques and datasets. For example, computer vision involves image segmentation, object detection, and pose estimation techniques. In NLP you can dive into transformers , pre-trained models and LLMs.
• Stay updated on the latest research: Follow research papers, blogs, and conferences in your chosen domain to stay ahead of the curve.

Additional Tips for Stage 3:

• Practice, Practice, Practice: The best way to solidify your understanding is to apply your knowledge to real-world problems. Participate in Kaggle competitions, work on personal projects, or contribute to open-source ML projects.
• Sharpen your coding skills: A strong foundation in Python and libraries like NumPy, Pandas, scikit learn, TensorFlow, and PyTorch is crucial for implementing ML algorithms from scratch or using existing libraries effectively. Also if you aim to be a Machine Learning Engineer it is important to lean about “Distributed ML” and practice some open sources like Spark, Dask, Polars, and RAPIDS as well as cloud computing such as "Google Cloud Platform" services which offer distributed computing capabilities and MLOps tools such as MLflow.
• Build a network: Connect with other ML enthusiasts, data scientists, and researchers and communities (Kaggle) . Online forums, meetups, and conferences are excellent platforms for learning from others and staying updated on the latest trends.

Remember, Stage 3 is a continuous journey of exploration and learning. As you delve deeper into the world of ML, you’ll discover new areas of interest and challenges to tackle. Embrace the ongoing learning process, and you’ll be well on your way to becoming a skilled and versatile Machine Learning practitioner.

Kaggle Competitions that Required Deep Understanding of Foundational Theories

Kaggle, a popular platform for machine learning competitions, offers challenges that vary in difficulty and the level of theoretical understanding required.

In the past eight years, I’ve participated in many of Kaggle’s most featured and challenging competitions. What I’ve learned from Kaggle and other Kagglers, especially the Grandmasters, goes beyond what I could have learned from just reading the best ML theory, statistical learning, or other foundational theory books. This is because Kaggle throws you at real-world problems and data, requiring you to develop practical solutions collaboratively. However, many competitions have also highlighted the importance of a strong foundation in the underlying theories and concepts that drive machine learning algorithms. This understanding is crucial for developing efficient and applicable solutions.

While I wouldn’t necessarily call myself a Kaggle Competition Master (it’s a long and challenging journey!), my experience has shown that a solid foundation in ML theory, statistics, and related mathematics is crucial for tackling certain Kaggle problems and competitions. Here are some examples:

1. RSNA — Intracranial Hemorrhage Detection

This challenge focuses on detecting intracranial hemorrhage (bleeding in the brain) from medical scans. Accurate medical diagnosis relies on robust image analysis models.

• Why Theory Matters: Dealing with medical data necessitates careful handling of biases and ensuring model generalizability. Statistical hypothesis testing helps assess the model’s performance on unseen data. Understanding transfer learning allows you to leverage pre-trained models efficiently for medical image analysis tasks.

2. M5 Forecasting — Accuracy (m5-forecasting-accuracy)

This competition focuses on time series forecasting, where you predict future values based on historical data (e.g., predicting sales figures for a retail store).

• Why it’s good for theory: Understanding time series analysis concepts like seasonality, trend analysis, and anomaly detection is essential. Additionally, knowledge of statistical methods for evaluating forecasting performance will be valuable. You might also explore advanced techniques like deep learning for time series forecasting (LSTMs).

3. Jigsaw Unintended Bias in Toxicity Classification

This challenge involves classifying news articles as real or fake.

Why it’s good for theory: This competition emphasizes Natural Language Processing (NLP) techniques like text classification and potentially recurrent neural networks (RNNs) for handling sequential data like sentences. Understanding how to evaluate the model’s performance on metrics like Bias AUCs (Nuanced Metrics for Measuring Unintended Bias with Real Data in Text Classification):Subgroup AUC, BPSN (Background Positive, Subgroup Negative) AUC and BNSP (Background Negative, Subgroup Positive) AUC

Additionally, exploring techniques to mitigate bias in NLP models can be valuable.

4. Santander Customer Transaction Prediction (santander-customer-transaction-prediction)

Predicting future customer transactions based on historical transaction data. This involves understanding customer behavior patterns and financial forecasting techniques.

• Why Theory Matters: Time series analysis is crucial to identify trends and seasonality in transaction data. Probability distributions knowledge and feature engineering plays a significant role in creating informative features from transaction details. Knowledge of statistical methods like survival analysis can be beneficial for predicting the likelihood of future transactions. Additionally, deep understanding ensemble methods like Random Forests or XGBoost allows you to combine multiple models for improved performance.

5. Medical Diagnosis and Image Recognition (competition example: RSNA Pneumonia Detection Challenge)

In this competition, you’re challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs. To overcome this challenge, a strong foundation in deep learning architectures for computer vision (particularly Convolutional Neural Networks or CNNs), medical imaging analysis, transfer learning techniques, and statistical hypothesis testing is necessary.

• Why deep understanding matters: Accurate medical diagnosis relies on robust image analysis models.expand_more Understanding how CNNs work, how to choose the right architecture for the task, and how to prevent overfitting on medical datasets (often limited in size) is crucial. Statistical hypothesis testing helps assess the generalizability of the model’s performance.

5. CAFA 5 Protein Function Prediction

Proteins are the building blocks of life, and their structure determines their function. This competition focuses on predicting the 3D structure of a protein based on its amino acid sequence. But protein folding is a complex biophysical process.

• Foundational Theories Needed: Molecular Biology, Protein Folding Simulations, Physics-based Machine Learning, Deep Learning for Protein Structure Prediction.
• Why Theory Matters: A solid grasp of molecular biology helps you understand the relationship between protein sequence and structure. Knowledge of protein folding simulations provides context for the problem. Physics-based machine learning allows incorporating physical constraints into the model. Deep learning architectures specifically designed for protein structure prediction are crucial for achieving high accuracy.

6. Netflix Prize (held by Netflix)

While no longer active, the Netflix Prize was a landmark competition that significantly advanced recommender systems. The challenge was to develop a system that could outperform Netflix’s existing recommendation algorithm for predicting user movie ratings.

• Theoretical Depth Required: This competition demanded expertise in collaborative filtering techniques, matrix factorization approaches, and potentially incorporating content-based filtering methods. Additionally, a solid understanding of statistical methods for evaluating recommender system performance (e.g., Root Mean Squared Error) was crucial.
• Why Theory Matters: Building a robust recommendation system requires a deep understanding of how user preferences and item characteristics interact. Knowledge of statistical methods for handling sparse data (most users haven’t rated most movies) was essential for accurate predictions.

References for Kaggle Winning Solutions Analysis

Once you’ve familiarized yourself with the competition concepts, explore the winning solution write-ups on Kaggle. These often provide insights into the specific techniques and theoretical considerations employed by top performers.

Winning solution of kaggle competition