DATA SCIENCE - Machine Learning
ITALIAN LANGUAGE
ASYNCHRONOUS COURSE DURATION: 18 HOURS
SYNCHRONOUS COURSE DURATION: 23 HOURS
MODE: online or in person
OBJECTIVES
Provide an overall picture of Machine Learning, from the basics to the most advanced techniques.
RECIPIENTS
All those who carry out or intend to carry out the activity of Data Scientist and Machine Learning Engineer. Degree in STEM disciplines preferred, Programming with Python, Descriptive Statistics for Data Scientists, Inferential Statistics for Data Scientists
SUBJECTS
Machine Learning Fundamentals
- Classify a Machine Learning problem
- Clean and prepare a dataset appropriately (missing values, duplicates, outliers, categorical variables)
- Build machine learning models for regression problems with linear regression
- Building machine learning models for classification problems with logistic regression
- Build machine learning models to perform clustering with the K-means algorithm
- Evaluate a machine learning model using the right metrics
Machine Learning: Models and Algorithms
- Understand main optimization algorithms and how to choose the correct one
- Create online learning systems capable of continuously learning and improving
- Learn to use all the main supervised machine learning models
- Building Artificial Neural Network models using scikit-learn
Machine Learning: Advanced Techniques
- Automate hyperparameter search using algorithms such as Grid Search and Random Search
- Use AutoML with Microsoft's FLAML framework
- Create and export an MLOps pipeline with joblib and pickle
- Understanding time series and forecasting using the Facebook Prophet algorithm
- Reduce the dimensionality of a dataset using techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)
- Understand the main computer vision techniques to classify images and recognize people and objects within them using Open CV
- Building recommender systems
CONTENTS AND TEACHING BY PROFESSION AI
