Data Science and Machine Learning with Python

In this course, modern data science and machine learning concepts and models are provided along with the fundamentals of programming and statistics. The aim of the course is to allow the students to gain practicality with practices and examples.  

There is no prerequisite for the course. 


Delegates will learn: 
Python ve Veri Bilimi Kütüphaneleri
İstatistik ve Keşifsel Veri Analizi
Regresyon ve Sınıflandırma Problemleri
Temel Makine Öğrenimi Algoritmaları
Gözetimsiz Öğrenme

Python and Data Science Libraries

Installations 

Python Basics 

Data Structures 

Conditional Expressions and Cycles 

File Operations, Functions, Errors and Modules 

NumPy 

Pandas: Excel in the Python World 

Visualization with Matplotlib  


Statistical and Exploratory Data Analysis

Basic Statistics Concepts 

Probability Theory  

Statistical Distributions 

Population, Sample and Related Theorems 

Data Cleaning 1: Variable Types  

Data Cleaning 2: Missing Values  

Data Cleaning 3: Extreme Values  

Exploratory Data Analysis 1: Univariate Analysis   

Exploratory Data Analysis 2: Multivariate Analysis  

Feature Engineering 1: Data Modification 

Feature Engineering 2: Data selection and Dimension Reduction 


Supervised Learning 1 - Regression and Classification Problems

What is Regression?  

Basic Linear Regression and OLS  

Linear Regression Assumptions  

Understanding the Relationship Between the Target Variable and Features  

Measuring the Training Performance of the Regression Model  

Estimation by Linear Regression  

Extreme Compatibility and Regularization 

What is Classification?  

Classification by Logistic Regression   

Measuring Training Performance of Classification Models (Error Matrix) 

Unbalanced Class 

Naive Bayes 


Supervised Learning 2 - Basic Machine Learning Algorithms

Classification with KNN  

Regression with KNN  

Decision Trees  

Random Forests  

Classification with Random Forests 

Regression with Random Forests 

Decision Support Machines 

Classification with Decision Support Machines 

Regression with Decision Support Machines  

Gradient Boosting 

Classification with Gradient Boosting

Regression with Gradient Boosting


Unattended Learning

What is Unattended Learning?  

Kmeans 

Spectral Clustering 

Mean-shift 

Affinity Propagation 

How to Measure the Performance of Clustering Algorithms?


There are no prerequisites for this course.

Program Details
Duration 5 Days
Capacity Max 12 Persons
Training Type Classroom / Virtual Classroom


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