Machine Learning in Python – Theory & Implementation

Machine Learning in Python - Theory & Implementation

Machine Learning in Python – Theory & Implementation

Learn Machine Learning Algorithms and their Python Implementations for your Data Science career.

Language: english

Note: 4.4/5 (12 notes) 5,697 students  New course 

Instructor(s): Onur Baltacı

Last update: 2022-11-02

What you’ll learn

  • Learn the theories behind the Machine Learning Algorithms
  • Learn applying the Machine Learning Algorithms in Python
  • Learn feature engineering
  • Learn Python fundamentals

 

Requirements

  • No requirements. Just willingness to learn is enough.

 

Description

Welcome to the Machine Learning in Python – From A to Z course. This course aims to teach students the machine learning algorithms by simplfying how they work on theory and the application of the machine learning algorithms in Python. Course starts with the basics of Python and after that machine learning concepts like evaluation metrics or feature engineering topics are covered in the course. Lastly machine learning algorithms are covered. By taking this course you are going to have the knowledge of how machine learning algorithms work and you are going to be able to apply the machine learning algorithms in Python. We are going to be covering python fundamentals, pandas, feature engineering, machine learning evaluation metrics, train test split and machine learning algorithms in this course. Course outline is

  • Python Fundamentals

  • Pandas Library

  • Feature Engineering

  • Evaluation of Model Performances

  • Supervised vs Unsupervised Learning

  • Machine Learning Algorithms

The machine learning algorithms that are going to be covered in this course is going to be Linear Regression, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests and K-Means Clustering. If you are interested in Machine Learning and want to learn the algorithms theories and implementations in Python you can enroll into the course. You can always ask questions from course Q&A section. Thanks for reading the course description, have a nice day.

 

Who this course is for

  • People who wants to learn Machine Learning
  • People who wants to learn Python

 

Course content

  • Welcome
    • Before Starting
  • Python Fundamentals
    • Print & Comments
    • Variables part 1
    • Variables part 2
    • Data types part 1
    • Data types part 2
    • Operators
    • If Statements
    • Loops
    • Functions
  • Pandas
    • Pandas
    • Pandas 2
    • Pandas 3
  • Feature Engineering
    • Feature Scaling
    • Feature Scaling in Python
    • Label Encoding
    • One Hot Encoding
    • Outlier Detection
  • Evaluation of the Model Performances
    • Train-Test Split
    • MSE – RMSE
    • Confusion Matrix – Accuracy Score
  • Machine Learning – Supervised vs Unsupervised
    • Supervised vs Unsupervised Machine Learning
  • Data Set Analysis & Feature Engineering for Regression Tasks
    • Data Set
    • EDA
    • Feature Engineering
  • Data Set Analysis & Feature Engineering for Classification Tasks
    • Data Set
    • EDA
    • Feature Engineering
  • Linear Regression
    • Linear Regression
    • Linear Regression 2
    • Linear Regression 3
    • Linear Regression Coding
  • Logistic Regression
    • Logistic Regression
    • Logistic Regression Coding
  • K Nearest Neighbors
    • K Nearest Neighbors
    • K-Nearest Neighbors Coding (Elbow Method)
    • K-Nearest Neighbors Coding
  • Support Vector Machines
    • Support Vector Machines
    • Support Vector Regression Coding
    • Support Vector Classifier Coding
  • Decision Tree
    • Decision Tree
    • Decision Tree Coding
  • Random Forest
    • Random Forest
    • Random Forest Regression Coding
    • Random Forest Classification Coding
  • K-means Clustering
    • K-means Clustering
    • K-means Clustering Coding

 

Machine Learning in Python - Theory & ImplementationMachine Learning in Python - Theory & Implementation

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