Machine Learning & AI — Complete Guide
From linear regression to deployed deep learning models you can defend in an interview.
Lifetime access · Certificate included · US timezone sessions
The 30-minute demo is free, needs no card, and carries no obligation. If we aren't the right fit for your goals, we'll tell you.
- Tutorials finished, but nothing deployed you can show
- A resume that lists coursework instead of shipped work
- Freezing when an interviewer asks you to explain a decision
- Unsure which skills US employers actually screen for
- 4 deployed projects with real source code
- A US-format resume built around what you shipped
- Mock interviews with written feedback, before the real one
- A clear path toward Machine Learning Engineer
Who this course is for
This course was designed around specific situations. If one of these sounds like you, you're in the right place.
What you'll learn
By the end of this course you will be able to do each of the following in real projects, not just in exercises.
Choose the right algorithm for a problem and justify the choice
Engineer features that actually improve model performance
Train, tune and evaluate models without fooling yourself
Build neural networks for vision and language tasks
Deploy models behind an API with monitoring and versioning
Your learning roadmap
The course runs in phases. Each one ends with a concrete milestone, so you always know whether you're on track.
- 1
Classical Machine Learning
Weeks 1–5Focus: Fundamentals that still win
What you'll do
- Implement regression, classification and clustering end to end
- Understand bias-variance tradeoff and regularisation
- Build robust cross-validation and avoid data leakage
- Master tree ensembles: random forests and gradient boosting
MilestoneA tuned gradient-boosting model beating a strong baseline on tabular data.
- 2
Feature Engineering & Evaluation
Weeks 6–8Focus: Where real gains come from
What you'll do
- Engineer features from dates, text, categories and interactions
- Handle imbalanced classes honestly
- Choose metrics that reflect the actual business cost
- Interpret models with SHAP and permutation importance
MilestoneA model with a documented evaluation and interpretability report.
- 3
Deep Learning
Weeks 9–12Focus: Neural networks
What you'll do
- Build and train networks in both TensorFlow and PyTorch
- Apply CNNs to image classification and transfer learning
- Use transformers for text classification and embeddings
- Diagnose training problems: vanishing gradients, overfitting, bad initialisation
MilestoneA fine-tuned vision model and a fine-tuned language model.
- 4
Deployment & MLOps
Weeks 13–14Focus: Production
What you'll do
- Serve models behind a FastAPI service in Docker
- Track experiments and version models with MLflow
- Monitor for data and concept drift
- Write a model card covering limitations and failure modes
MilestoneA deployed, monitored, documented model serving live predictions.
Everything you'll cover
A map of the whole curriculum at a glance — every major area and the topics inside it.
- Linear/logistic regression
- Decision trees
- Random forests
- Gradient boosting
- SVMs
- K-means
- Hierarchical clustering
- PCA
- Anomaly detection
- Neural networks
- CNNs
- RNNs
- Transformers
- Transfer learning
- Encoding
- Scaling
- Interactions
- Text features
- Leakage avoidance
- Cross-validation
- Precision/recall
- ROC-AUC
- SHAP
- Calibration
- FastAPI serving
- Docker
- MLflow
- Drift monitoring
- Model cards
Week-by-week syllabus
A structured breakdown of exactly what you'll cover, week by week.
Projects you'll build
You finish with a portfolio of deployed work — the thing hiring managers actually look at.
Customer Churn Predictor
End-to-end tabular ML with feature engineering, tuning and business-cost-aware metrics.
Image Classifier with Transfer Learning
Fine-tune a pretrained CNN on a custom dataset and analyse failure cases.
Text Sentiment & Embeddings
Fine-tune a transformer for classification and build a semantic search index.
Deployed Prediction API
Serve a model via FastAPI in Docker with experiment tracking and drift monitoring.
Where this course can take you
Typical roles this course prepares you for, with current US market compensation ranges.
| Role | US salary range | Demand |
|---|---|---|
| Machine Learning Engineer | $130,000 – $190,000 | Very high |
| Data Scientist | $110,000 – $165,000 | Very high |
| AI Engineer | $125,000 – $180,000 | Very high |
| Research Engineer | $140,000 – $200,000 | Selective |
Salary ranges are indicative market figures for reference, not guarantees of employment or compensation.
Prerequisites
- Solid Python and Pandas — our Python for Data Science course or equivalent
- Comfort with basic statistics and linear algebra concepts
- Familiarity with Jupyter notebooks
Tools you'll use
Frequently asked questions
How much maths do I really need for machine learning?
Less than most people fear for applied roles. You need comfort with linear algebra concepts, derivatives at an intuitive level, and solid statistics. The course teaches the maths inline as each algorithm needs it, focused on understanding behaviour rather than deriving proofs. Research roles demand more depth.
TensorFlow or PyTorch — which should I learn?
The course covers both because job postings differ. PyTorch dominates research and has become the default in most new US industry projects; TensorFlow remains common in established production systems and on mobile. Concepts transfer almost completely between them.
Is machine learning still a good career choice given AI tools?
The demand has shifted rather than shrunk. Roles increasingly emphasise deploying, evaluating and integrating models rather than training from scratch. That is exactly why this course spends its final phase on deployment, monitoring and interpretability rather than stopping at model training.
What is data leakage and why does the course emphasise it?
Data leakage is when information unavailable at prediction time slips into training, producing models that look excellent in testing and fail in production. It is the single most common serious mistake in junior ML work, so it is treated as a first-class topic throughout.
Do I need a GPU?
No. All exercises run on free Google Colab GPU tiers, and the deep learning phase is designed around those constraints. Local GPU setup is covered for students who have one.