# A to Z’ye Machine Learning, with Python

## You can use the following tutorial for the topics and related code, dataset and other details described in the course.

• Section 1 Introduction: Welcome
• Lesson 1: Machine Learning and Applications
• Lesson 2: Machine Learning and the Future of the Future
• Lesson 3: Installing Python and Anaconda
• Lesson 4: Important notes about the courses and course order.
• Section 2 : Data Preprocessing
• Lesson 5: Loading of data
• Lesson 6: Loading Libraries: NumPY, Pandas and MathPlot Loading
• Lesson 7: Data Import
• Lesson 8: Python: Object Oriented Programming
• Lesson 9: Missing Data
• Lesson 10: Categorical Data
• Lesson 11: Consolidation of data sets and DataFrame concept
• Lesson 12: Dividing the Data Set into Training and Testing
• Lesson 13: Attribute scaling
• Lesson 14: Data Pre-processing Template
• Quiz 1: Data Pre-prosessing Template
• Section 3: Prediction
• Lesson 15: Forecast Problems and General Introduction
• Section 3.1: Simple Linear Regression
• Lesson 16: Downloading the Data Set
• Lesson 17: Data Set and Codes
• Lesson 18: Introduction to Linear Regression
• Lesson 19: The Data Loading with the Data Preprocessing Template
• Lesson 20: Construction of Linear Regression Model
• Lesson 21: Estimation by applying Linear Regression Model
• Lesson 22: Linear Regression and Visualization of Data
• Quiz 2: Simple Linear Regression Questions
• Section 3.2: Multiple Linear Regression
• Lesson 23: Data Set and Problem Definition
• Lesson 24: Problems and Solutions in Multiple Variables
• Lesson 25: Dummy Variable and Puppet Variable Trap
• Lesson 26: Homework 1: P-Value
• Lesson 27: P-Value
• Lesson 28: Variable Selection and Backward Elimination, Forward Selection, Bidirectional Elimination methods
• Lesson 29: Multiple Linear Regression Coding: Preparing a Data Set
• Lesson 30: Multiple Linear Regression Coding: Regression Model
• Lesson 31: Backward Elimination
• Assignment 1: Multiple Linear Regression (Lesson 32)
• Assignment 1: Solutions Part 1: Data Preparation and Linear Regression (Lesson 33)
• Assignment 1: Çözümü Part 2: Backward Elimination (Lesson 34)
• Quiz 3: Multiple Linear Regression
• Section 3.3: Polynomial Regression
• Lesson 35: Data Set, Definition of Concept and Problem
• Lesson 36: Application of Polynomial Regression with Python
• Lesson 37: Polynomial Regression Template with Python
• Section 3.4: Support Vector Regression, SVR
• Lesson 38: Data Set, Definition of Concept and Problem
• Lesson 39: SVR application with Python
• Section 3.5: Prediction with Decision Tree
• Lesson 39: Data Set, Definition of Concept and Problem
• Lesson 40: Python encoding of Decision Tree Regression
• Section 3.6: Forecast with Random Forest
• Lesson 39: Data Set, Definition of Concept and Problem
• Lesson 40: Random Tree Regression with Python
• Section 3.7: Evaluation of Predictions
• Lesson 41: R-Square method
• Lesson 42: Adjusted R-Square method
• Lesson 43: Evaluation of Linear Regression Multipliers
• Assignment 2: Evaluating Forecasting Algorithms
• Assignment 2: Solutions
• Forecast Part Summary and Comparison of Models
• Summary: Comparing Estimation Methods
• Template: Estimation Methods (SRL, MLR, PR, SVM, DT, RF) + R2 Calculation + Correlation
• Section 4: Classification
• General Introduction to Classification Problems
• Section 4.1: Logistic Regression
• Introduction to Logistic Regression
• Logistic Regression Coding with Python
• Confusion Matrix and Classification template
• Section 4.2: K-NN
• K-NN Algorithm
• K-NN encoding with Python
• Distance Metrics
• Python Code: Distance algorithms
• Quiz: K-NN
• Section 4.3: Support Vector Machine
• Introduction to Comprehension and Problems
• SVM practice and classification with Python
• Section 4.4: Support Vector Machines Core usage
• Kernel trick
• Python and Kernel functions and SVM
• Section 4.5: Naive Bayes
• Bayes’ theorem, Algorithm for Naive Bayes and definition of problems, Numerical solution
• Coding of Naive Bayes algorithm with Python
• Section 4.6: Decision Trees
• Introduction to decision trees and classification problems
• Coding decision trees with Python
• Section 4.7: Random Forest
• Application of Random Forests to classification problems
• Random Forest coding with Python
• Section 4.8: Evaluation of Classification Algorithms
• Confusion Matris
• Flase Positive ve False Negative Concepts
• Clarity / accuracy paradox
• ROC ecurve
• Section 4.9: Template and Assignment
• Classification Template
• Assignment 3: Iris data set classification
• Assignment 3: Solution and Algorithm Comparison
• Python Code: solution of assigment 2
• Comparisons of Classification Algorithms and Summary
• Section 5: Clustering
• Summary Table
• Section 5.1: K-Means
• Introduction to algorithm and algorithm
• Random Start Trap
• Decision of cluster number in K-Means algorithm
• Coding of K-Means algorithm with Python
• Test : K-Means
• Section 5.2: Hierarchical clustering
• Introduction to the concept of Hierarchical Clustering
• Dendrogram concept and use of hierarchical clustering
• Hierarchical clustering with Python
• Test: Hierarchical Clustering
• Section summary and Comparison of Models
• Section 6: Association Rule Mining
• Section 6.1: Apriori Algorithm
• Apriori Rule Inference and Introduction to Algorithm and operation of the algorithm
• Python encoding of the algorithm
• Section 6.2: Eclat Algorithm
• Operation of the algorithm
• Section 7: Reinforced Learning
• Introduction to Reinforced Learning Concept
• Section 7.1. UCB Model Selection
• Multi Armed Bandit Problem
• Upper Confidence Bound, UCB Approach
• UCB Algorithm
• Python encoding of the algorithm
• Datas
• Python Code
• Section 7.2. Thompson Sampling
• Thompson Sampling Approach
• Comparison of Algorithms (UCB and Thompson Sampling)
• Python Code
• Section 8: Natural Language Processing and Text Processing
• Section 9: Deep Learning
• Section 10: Dimensionality Reduction and Dimension Transformation
• Section 11: Model Selection
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