Technology & Engineering
Machine Learning
Build, train, and evaluate predictive models from first principles to deployment.
Curriculum
Your six-week path
Each week meets once and builds on the previous session, ending in a hands-on capstone.
- 1Week 1
Foundations of Machine Learning
Understand what ML is and set up your working environment.
- Supervised vs. unsupervised vs. reinforcement learning
- The ML workflow: data, model, evaluation, iteration
- Python, NumPy, and pandas for ML
- Setting up notebooks and reproducible experiments
- 2Week 2
Data Preparation & Feature Engineering
Turn raw data into model-ready features.
- Cleaning, handling missing values, and outliers
- Encoding categorical variables and scaling
- Feature selection and dimensionality reduction
- Train/validation/test splitting strategies
- 3Week 3
Supervised Learning
Master core regression and classification algorithms.
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines and k-NN
- Bias–variance tradeoff and regularization
- 4Week 4
Model Evaluation & Tuning
Measure and improve model performance rigorously.
- Accuracy, precision, recall, F1, ROC–AUC
- Cross-validation techniques
- Hyperparameter tuning with grid and random search
- Diagnosing underfitting and overfitting
- 5Week 5
Unsupervised Learning & Neural Networks
Explore clustering and an intro to deep learning.
- K-means and hierarchical clustering
- Principal component analysis (PCA)
- Introduction to neural networks and backpropagation
- Building a first model with a deep learning framework
- 6Week 6
Deployment & Capstone
Ship a model and present an end-to-end project.
- Serializing models and building an inference API
- Monitoring, drift, and responsible AI
- MLOps basics and versioning
- Capstone: end-to-end ML project presentation
Levels
Choose the level that fits you
Machine Learning is offered at five levels. Sign up for the one that matches your current experience.
- 1
Beginner
Start from the fundamentalsFor students who are new to the subject. Covers core concepts, key terms, and the basics you need to get started with confidence.
- 2
Intermediate
Build on what you knowFor students with some prior knowledge. Goes deeper into practical skills, tools, and real-world application of the fundamentals.
- 3
Advanced
Master complex skillsFor experienced students ready for challenging material, advanced techniques, and independent problem-solving.
- 4
Professional
Industry-ready expertiseFor working professionals. Focuses on workplace scenarios, best practices, and the standards expected in the field.
- 5
Diploma
Full certification trackA comprehensive track combining all levels toward a Fisko Foundation diploma, with a final assessment and certificate of completion.
Test your knowledge
A quick 4-question check on Machine Learning. Pick an answer for each question, then submit to see your score.
1. Which type of learning uses labeled data to make predictions?
2. What does 'overfitting' mean?
3. Which metric is used for classification performance?
4. What is the first step in a typical ML workflow?
0 of 4 answered
Ready to enroll in Machine Learning?
Secure your place in the next six-week cohort and start building practical skills one day a week.
Enroll now