My journey in ML has been anything but traditional. I started with a project-based approach focusing on projects over theory. This “code-first, theory-later” method helped me learn ML in a practical, hands-on way.

I learn best out of necessity. When faced with a challenge, I find resources, understand them, and take action. This “learn as you go” approach keeps my journey practical and focused on my career goals.

Projects Open Doors

All my internships so far have come from projects I built, not just theoretical knowledge. Showing real applications has helped me stand out, proving that a project-first approach is key to transitioning from theory to real-world impact.

Key Steps in My ML Journey

1. Building a Strong Foundation in Math

Not all ML roles require deep math expertise, especially in ML engineering. For those like me who lean toward engineering over research, here’s the essential math I recommend:

• Math Basics: If you’ve studied JEE-level math, you’ve likely got the basics you need. Focus on matrices, calculus, probability, and linear algebra.

2. Learning Python and Core Libraries

Python is essential in ML, so getting comfortable with it early is critical. I recommend any beginner Python course (like FreeCodeCamp’s) to establish a solid foundation in syntax and core concepts.

• Core Libraries: Familiarize yourself with key ML libraries:
• NumPy: Essential for numerical computing and working with arrays.
• Pandas: Ideal for data manipulation and analysis.
• Matplotlib & Seaborn: Provides tools for data visualization and insights.
• scikit-learn: Core ML library for implementing algorithms, model selection, and evaluation.

3. Understanding Algorithms

• Concise Resources: Begin with the 100-Page ML Book for a clear introduction to key algorithms without overwhelm.
• Hands-On Coding: Writing algorithms from scratch deepens practical understanding.

Core Algorithms:

Clustering: K-Means,Hierarchical Clustering

Visualization & Dimensionality Reduction: PCA,t-SNE

Anomaly Detection: Isolation Forest,Local Outlier Factor

Supervised Learning Algorithms:

Regression: Linear Regression,Logistic Regression

Classification:K-Nearest Neighbors (KNN),Decision Trees,Random Forests,Support Vector Machines (SVM),Gradient Boosting Machines (GBM)

Model Evaluation & Validation:

Cross-Validation

Performance Metrics (Accuracy, Precision, Recall, F1 Score, MSE)

Stanford’s ML courses are great but can be heavy on math so they might feel a bit tough at first. Once you get the hang of it, you’ll have a strong advantage.

Moving to the Next Level: Projects, Competitions & Community Engagement

Projects: Deploy, Compete, Iterate

Building personal projects is critical, but don’t stop there — deploy them, measure their success, and, if possible, make them public. This allows you to get valuable feedback and learn from real-world challenges.

Hackathons, Kaggle Competitions, and Networking

• Hackathons: They force you to apply ML practically under time constraints, which improves skills and showcases your capability.
• Kaggle: Study past winning solutions to learn from how others tackle ML problems.
• Networking: Connect with ML enthusiasts on Twitter, reach out to mentors, and actively seek feedback. A mentor can guide your journey, offer insights, and open new doors.

Deployment Skills: Making Projects Accessible

Deploying projects is essential to bridge the gap between building and sharing ML applications.

Frameworks:

• Flask: Lightweight framework for serving models via APIs.
• Django: Robust framework for larger applications.

Cloud Services: Use platforms like AWS, Azure, or GCP for efficient deployment and scaling.

Quick App Deployment:

• Streamlit and FastAPI: Ideal for quick app deployment and high-performance API creation.

Containerization:

• Docker and Kubernetes: For streamlined containerization and orchestration in deployment.

Goal Clarity: ML Engineers vs. ML Research

Knowing your career goal is essential. If you aim to be an ML engineer like me, focus on application rather than diving into theory. This clarity allows you to focus on skills that directly impact your career path.

Deep Learning: When to Dive Deeper

Once you’re comfortable with ML fundamentals, deep learning can be a valuable next step. Start with applied ML to build a strong foundation, and only move into deep learning when your projects require it. As you tackle more complex projects — like working with text, images, or recognizing intricate patterns — you’ll feel the urge to explore the deep learning side of applied ML. This transition is like stepping up to a more advanced toolkit that can handle sophisticated tasks with greater ease.

1. Start with Simple Neural Networks
Begin by creating basic neural networks, the building blocks of deep learning. Get familiar with layers, neurons, and how they work together to learn from data. A fun starter project is building a digit classifier using the MNIST dataset. This project helps you see how data flows through the network and how it learns to make predictions.

Explore CNNs and RNNs

Convolutional Neural Networks (CNNs): Perfect for image-related tasks, CNNs recognize patterns and features in images. Try classifying images of animals or objects to see how CNNs detect details.

• Supplementary: CS231n: Deep Learning for Computer Vision

Recurrent Neural Networks (RNNs): Ideal for sequence-based data, RNNs are great for tasks like generating text or analyzing sentiment. A simple chatbot can be a fun way to understand how RNNs grasp context.

Articles:

• Understanding LSTM Networks — colah’s blog
• PyTorch RNN from Scratch — Jake Tae

2. Use Transfer Learning to Save Time
Learning deep learning from scratch can be time-consuming. Transfer learning allows you to adapt pre-trained models to your needs, saving you time and effort. For example, use a pre-trained model like ResNet for image recognition tasks to achieve impressive results quickly.

3. Advance to Specialized Models
Once you’re comfortable with the basics, dive into more advanced models that power amazing AI applications:

Generative Adversarial Networks (GANs): These models consist of two networks that compete against each other to create realistic images, unique artwork, or even music. With GANs, you can design projects that generate lifelike landscapes or original art.

Supplementary:Generative Deep Learning

Transformers: If you’re focusing on text, Transformers are revolutionary. Models like BERT and GPT excel at understanding and generating human-like language. They’re perfect for chatbots, language translation, and summarization, making your projects sound more natural and context-aware.

Videos:Coding a Transformer from scratch on PyTorch, with full explanation, training and inference.

Paper

• Attention Is All You Need
• The Illustrated Transformer
• The Annotated Transformer by Harvard
• Thinking like Transformer

Consider Other Advanced Techniques

• Autoencoders: Great for cleaning data and finding patterns. They can enhance noisy images or highlight key features.
• Reinforcement Learning: Ideal for building models that learn through experimentation, useful in robotics and strategy-based AI, like self-driving cars.

4. Recommended Reading

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A comprehensive resource on deep learning concepts.
• Pattern Recognition and Machine Learning by Christopher M. Bishop: Focuses on statistical techniques in machine learning.
• Deep Learning for Computer Vision with Python by Adrian Rosebrock: Practical applications of deep learning in computer vision

Building Real-World Applications

Applying your deep learning knowledge to real-world projects is one of the most effective ways to enhance your skills in applied machine learning. By tackling complex, practical challenges, you can broaden your understanding and refine your abilities.

Engaging in real-world applications allows you to translate theoretical concepts into tangible results. Each project presents unique challenges that encourage you to think critically, adapt to varying scenarios, and innovate solutions.

Conclusion

This project-first, code-driven approach to ML has been incredibly rewarding, both for my learning and career. I hope this roadmap offers you a structured yet flexible path to help you succeed in your ML journey!