Machine Learning Training
Machine Learning is the most sought-after AI skill. You'll learn the full ML workflow — from data wrangling and feature engineering through model selection, evaluation and deployment. The course uses real datasets from finance, healthcare and e-commerce so your portfolio is immediately employer-relevant.
Machine Learning is the most sought-after AI skill. You'll learn the full ML workflow — from data wrangling and feature engineering through model selection, evaluation and deployment. The course uses real datasets from finance, healthcare and e-commerce so your portfolio is immediately employer-relevant.
Syllabus
- Python for ML & Pandas
- Supervised Learning
- Unsupervised Learning
- Feature Engineering
- Model Evaluation & Tuning
- Ensemble Methods
- Time Series Forecasting
- Model Deployment with FastAPI
What you will learn
- Build end-to-end ML pipelines
- Tune and validate models like a pro
- Deploy models as REST APIs
- Communicate results to business stakeholders
Tools you will use
- Python
- Scikit-learn
- Pandas
- NumPy
- XGBoost
- FastAPI
- MLflow
Projects
- Customer churn prediction
- Credit risk scoring model
- Demand forecasting for retail
- Deployed ML API on cloud
Career paths
- ML Engineer
- Data Scientist
- Research Analyst
- Applied ML Engineer
Who should join
- Python developers
- Statistics & Math students
- Analysts moving into ML
Prerequisites
- Basic Python helpful (we cover from scratch)
- High-school math
- Curiosity for problem-solving
Certificate
AI specialization certificate with deployed real-world AI projects in your portfolio.
Syllabus
- Python for ML & Pandas
- Supervised Learning
- Unsupervised Learning
- Feature Engineering
- Model Evaluation & Tuning
- Ensemble Methods
- Time Series Forecasting
- Model Deployment with FastAPI
Learning outcomes
- Build end-to-end ML pipelines
- Tune and validate models like a pro
- Deploy models as REST APIs
- Communicate results to business stakeholders
Tools
- Python
- Scikit-learn
- Pandas
- NumPy
- XGBoost
- FastAPI
- MLflow
Projects
- Customer churn prediction
- Credit risk scoring model
- Demand forecasting for retail
- Deployed ML API on cloud
Career paths
- ML Engineer
- Data Scientist
- Research Analyst
- Applied ML Engineer
Who should join
- Python developers
- Statistics & Math students
- Analysts moving into ML
Prerequisites
- Basic Python helpful (we cover from scratch)
- High-school math
- Curiosity for problem-solving
Frequently asked questions
How is ML different from AI?
ML is the subset of AI focused on learning patterns from data — and it's where most jobs actually are today.
Which tools and technologies will I actually use?
You work hands-on with Python, Scikit-learn, Pandas, NumPy, XGBoost, FastAPI and MLflow throughout the course, rather than only studying them in theory.
What will I be able to do by the end?
By the end you can: Build end-to-end ML pipelines; Tune and validate models like a pro; Deploy models as REST APIs; Communicate results to business stakeholders.