AI & Machine Learning Engineering: From Foundations to Deep Learning, GenAI & MLOps
Instructor: Dr. Elena Rostovaelena.rostova@lms.com
01
Python for AI/ML
Python ecosystem and development setup, variables, data types & operators, conditions, loops and functions, lists, tuples, sets and dictionaries, strings, modules and packages, file handling and exception handling, object-oriented programming basics for ML projects. Practical: Build a Python-based data processing utility. Quiz: Module 1 Knowledge Check.
What's included
3 readings2 assignments1 Quiz
02
NumPy & Numerical Computing
03
Pandas & Data Manipulation
04
Data Visualization & Exploratory Data Analysis (• Practical Tab 1: Python & Data Analysis Lab)
05
Statistics & Probability for Machine Learning
06
Data Preprocessing & Feature Engineering
07
Supervised Learning — Regression
08
Supervised Learning — Classification (• Practical Tab 2: Classical Machine Learning Lab)
09
Unsupervised Learning
10
Model Evaluation & Selection
11
Scikit-learn ML Workflows
12
Deep Learning Fundamentals (• Practical Tab 3: Machine Learning & Deep Learning Lab)
13
TensorFlow / Keras
14
Natural Language Processing (NLP)
15
Computer Vision
16
Generative AI & Large Language Models (• Practical Tab 4: Applied AI Lab)
17
Model Explainability & Responsible AI
18
AI/ML Engineering Practices
19