# University of the Aegean – Advanced Python with applications in Machine Learning

## 21/02/2022

### Instructors

Spyros Kokolakis

Maria Karyda

Ioannis Stylios

### Object & Purpose of the Program

The purpose of the “Advanced Python with Applications in Machine Learning” program is to educate students in the Python programming language and Machine Learning. Learners will be introduced to Python Libraries for Machine Learning (Scikit-learn, Tensorflow, keras). They will learn to do Data Munging, Data Visualization and Predictive Analytics using Machine Learning algorithms. Important topics in Machine Learning will be presented and the trainees will know and implement Unsupervised Learning Algorithms such as: K-Means and Principal Component Analysis as well as Supervised Learning Algorithms Algorithms such as: Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Random Forests, K-Nearest Neighbor. Finally, they will learn to work with Neural Networks and Deep Learning methods (Convolution Neural Networks).

The program is aimed at anyone interested in certifying as a Learning Engineer using the Python language. The program is also aimed at professionals who want to improve their respective knowledge, degree holders or seniors who want to pursue a career as data scientists, teachers who want to attend a state-of-the-art program. Requires basic Python knowledge.

### Program Learning Objectives

Upon completion of the program, trainees will be able to:

- To do Data Munging.
- To do Data Visualization.
- To do Predictive Analytics using Machine learning algorithms.
- To work with Supervised Learning Algorithms such as: Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Random Forests, K-Nearest Neighbor.
- To work with Unsupervised Learning Algorithms such as: K-Means and Principal Component Analysis.
- To work with Neural Networks and Deep Learning methods.

### Syllabus

**Module 1: Important Topics in Machine Learning and Unsupervised Learning Algorithms**This section will introduce you to the Python programming language for machine learning. There will also be reference to Important Engineering Learning topics and we will see Unsupervised Learning Algorithms such as: K-Means and Principal Component Analysis.

**Week 1: Python and Machine Learning – Introduction to Python**

**Week 2: Machine Learning & Deep Learning Evaluation – Introduction to Python**

**Week 3: Python Libraries for Machine Learning (Pandas, NumPy, Matplotlib, Scikit-learn, Tensorflow, keras)**

**Week 4: Unsupervised Learning Algorithms (K-Means, Principal Component Analysis)**

**Module 2: Supervised Learning Algorithms – Neural Networks and Deep Learning**In this module students will learn Supervised Learning Algorithms such as: Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Random Forests, K-Nearest Neighbor. They will also learn to work with Neural Networks and Deep Learning methods.

**Week 5: Linear Regression, Logistic Regression, Support Vector Machines**

**Week 6: Decision Trees and Random Forests, K-Nearest Neighbor**

**Week 7: Neural Networks and Deep Learning: Convolution Neural Networks**

**Week 8: Mini project**