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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
6- 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
21- P_Value-Statistical Tests
22- Introduction to KNN Algorithm
23 - KNN-Exercises
Introduction to Artificial Neural Networks with python
Exercises in Artificial Neural Networks with python
25- K-Nearest Neighbors as Classifier and Regressor
26- Introduction of heteroscedasticity
27-Heteroscedasticity in Regression Analysis
28- Introduction to Polynomial Regression
29- Polynomial Regression Exercises
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
24- MySql Exam
Optimization
Introduction to Genetic Algorithms
Introduction to Expert Systems
Networks in Python
Technical Analysis with python
Reinforcement Learning
Introduction to Reinforcement Learning
7- Unsupervised Learning
Introduction to Unsupervised Learning
30- Introduction to K-Means Clustering
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Freda Dyrkaj
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Bug Data Trainer
Konstantinos Liagkouras
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