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S9_Machine Learning
S9_Machine Learning
47 Students enrolled
Created by: Freda Dyrkaj, Konstantinos Liagkouras
Last Updated: 19th September 2024
English
Requirements
S9_Machine Learning
Description
S9_Machine Learning
Content
1- Introduction to Machine learning
1- Introduction to Machine learning
2- Introduction to Python Language
2- Install Python - If-Else Statements/String Variables/Functions
3- Lists-Dictionaries
4- TuplesFiles-Exercises
3- Python libraries suitable for ML
5- Loading Data for ML-Pandas
6- Introduction to Analytics/NumPy
7- Data preparation with Numpy
8- Understanding data with Visualization
9- Exam in Python/Pandas/NumPy
4- Analytics and Visualization
10- Understanding data with Visualization
11- Advanced Analytics
12- Linear Regression
13- Multiple/Logistic Regression
14- Exam in Regression Analysis
Sentiment Analysis
Text Mining with Python
Multiclass Sentiment Analysis with python
Supervised Learning
Introduction to Naive Bayes
Naive Bayes for Text Classification
Introduction to Support vector machine (SVM)
Exercises Support vector Regression with python
Introduction to Time Series
Exercises in Time Series with python
5- Introduction to RDBMS-MYSQL
15- Introduction to DBMS
16- MySql-Query-Constraints-SelectWhere
17- MySql/Alias/Index/view
18- MySql- Joins/Having/Group by
19- Connect Python with MySql
20- Functions and Queries in PythonMySql
Optimization
Introduction to Genetic Algorithms
Reinforcement Learning
Introduction to Reinforcement Learning
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Freda Dyrkaj
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Konstantinos Liagkouras
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