ML Engineer

☐ Python · Required · 5–6 hrs

Functions, classes, list comprehensions, file I/O, virtual environments. Write clean, idiomatic Python before touching ML libraries.

• What is Python? Setup & Your First Program (Python): https://chaduvuko.com/learn/python/what-is-python-setup
• Functions — Defining, Parameters, Return Values (Python): https://chaduvuko.com/learn/python/functions
• Reading & Writing Files (Python): https://chaduvuko.com/learn/python/reading-writing-files
• Exception Handling (Python): https://chaduvuko.com/learn/python/exception-handling

☐ Math & Stats · Required · 8–10 hrs

Linear algebra (vectors, matrices, eigenvalues), calculus (gradients, chain rule), probability (Bayes, distributions). You need this to understand what algorithms actually do.

• Vectors, Matrices and Tensors (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/math-foundations/vectors-matrices-tensors
• Derivatives, Gradients and the Chain Rule (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/math-foundations/derivatives-and-gradients
• Probability Distributions and Bayes Theorem (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/math-foundations/probability-distributions

☐ SQL · Required · 4–5 hrs

JOINs, aggregations, window functions. Real ML jobs pull training data from databases, not CSVs on your laptop.

• Your First Query — SELECT & FROM (SQL): https://chaduvuko.com/learn/sql/select-from
• Introduction to Joins (SQL): https://chaduvuko.com/learn/sql/joins-intro
• Grouping Data — GROUP BY (SQL): https://chaduvuko.com/learn/sql/group-by
• Subqueries — Queries Inside Queries (SQL): https://chaduvuko.com/learn/sql/subqueries

☐ Git & GitHub · Required · 2 hrs

Version control for code and experiments. Pull requests, branching. Every team uses Git.

• Git and Version Control for Data Projects (Data Engineering): https://chaduvuko.com/learn/data-engineering/git-for-data

☐ NumPy & Pandas · Required · 5 hrs

Array math, DataFrame manipulation, groupby, merges. The daily bread of every data/ML job.

• NumPy Arrays and Broadcasting (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/programming/numpy-arrays
• Pandas for Data Analysis (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/programming/pandas-dataframes

☐ Scikit-learn · Required · 6 hrs

Linear/logistic regression, decision trees, SVMs, cross-validation, pipelines, GridSearchCV. Core classical ML toolkit.

• Scikit-learn Interface (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/programming/sklearn-interface
• Feature Engineering and the Sklearn Pipeline (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/data-engineering/feature-engineering

☐ Data Viz (EDA) · Recommended · 3 hrs

Matplotlib, Seaborn, Plotly. EDA before modelling catches data issues and impresses interviewers.

• Data Visualisation for ML (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/programming/matplotlib-seaborn

☐ Feature Engineering · Required · 5 hrs

Encoding, scaling, imputation, feature selection, target encoding. The single biggest lever for improving model performance on real data.

• Feature Engineering and the Sklearn Pipeline (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/data-engineering/feature-engineering
• Feature Scaling — Standardisation and Normalisation (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/data-engineering/feature-scaling
• Encoding Categorical Features (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/data-engineering/encoding-categorical-features

☐ PyTorch · Required · 10 hrs

Tensors, autograd, nn.Module, training loop, GPU usage. PyTorch is the default framework at top product companies.

• Neural Networks from Scratch (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/deep-learning/neural-networks-from-scratch
• Optimisers — SGD, Adam, AdamW (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/deep-learning/optimisers

☐ ML System Design · Required · 6 hrs

Feature stores, training pipelines, serving latency, retraining triggers, offline vs online. Asked in every senior ML interview.

• ML System Design — End to End (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/mlops/ml-system-design

☐ Docker · Required · 3 hrs

Containerise your model serving code. A model that only runs on your laptop is not a shipped model.

• Model Deployment — FastAPI, Docker, K8s (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/mlops/model-deployment

☐ MLOps · Required · 10 hrs

MLflow experiment tracking, model registry, CI/CD for ML, drift detection, retraining pipelines. The gap between notebook and production.

• Experiment Tracking with MLflow and W&B (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/mlops/experiment-tracking
• Model Deployment — FastAPI, Docker, K8s (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/mlops/model-deployment
• Model Monitoring — Drift Detection (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/mlops/model-monitoring
• Retraining Pipelines (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/mlops/retraining-pipelines

☐ LLMs & Fine-tuning · Recommended · 10 hrs

Transformers architecture, HuggingFace, LoRA fine-tuning, RLHF concepts. In-demand at every startup in 2025–26.

• LLMs — Pretraining, RLHF and Scaling Laws (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/generative-ai/llms-pretraining-rlhf
• LLM Fine-Tuning in Practice (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/generative-ai/llm-fine-tuning

☐ SageMaker / Vertex AI · Required · 8 hrs

Managed training jobs, endpoints, pipelines. Most enterprises expect cloud ML platform experience.

• AWS SageMaker — Training Jobs and Pipelines (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/cloud-ml/aws-sagemaker
• GCP Vertex AI — Pipelines and AutoML (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/cloud-ml/gcp-vertex-ai
• Azure ML — Studio, Pipelines and AutoML (AI & Machine Learning): https://chaduvuko.com/learn/ai-ml/cloud-ml/azure-ml