ML Journey: Basics to Advanced

A structured roadmap for learning Machine Learning from the ground up.

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Goal

Build a strong foundation in ML concepts and progressively develop the skills needed to design, train, and deploy production-ready models.

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Phase 1: Prerequisites

• Python (NumPy, Pandas, Matplotlib)
• Linear Algebra (vectors, matrices, dot products)
• Statistics and Probability (mean, variance, distributions, Bayes theorem)
• Calculus basics (derivatives, chain rule)

Phase 2: Core Machine Learning

• Supervised Learning: regression, classification
• Unsupervised Learning: clustering, dimensionality reduction
• Model Evaluation: train/test split, cross-validation, metrics
• Common Algorithms: Linear Regression, Logistic Regression, Decision Trees, SVM, KNN, Random Forest

Phase 3: Deep Learning

• Neural Networks: perceptrons, activation functions, backpropagation
• Frameworks: TensorFlow or PyTorch
• CNNs for image tasks
• RNNs and LSTMs for sequential data
• Transfer Learning and fine-tuning

Phase 4: Specialized Topics

• Natural Language Processing (NLP): tokenization, embeddings, transformers
• Computer Vision
• Reinforcement Learning basics
• Generative Models: GANs, VAEs, Diffusion Models

Phase 5: MLOps and Production

• Data pipelines and feature engineering
• Experiment tracking (MLflow, Weights and Biases)
• Model deployment (FastAPI, Docker, cloud platforms)
• Monitoring and retraining strategies

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Resources

• Books: "Hands-On ML" by Aurelien Geron, "Deep Learning" by Goodfellow et al.
• Courses: Coursera ML Specialization (Andrew Ng), Andrej Karpathy Youtube Playlist
• Practice: Kaggle competitions, personal projects, open datasets

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