Machine Learning In Python - From A To Z Machine Learning

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

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 isPython FundamentalsPandas LibraryFeature EngineeringEvaluation of Model PerformancesSupervised vs Unsupervised LearningMachine Learning AlgorithmsThe 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.

Overview

Section 1: Python Fundamentals

Lecture 1 Print & Comments

Lecture 2 Variables part 1

Lecture 3 Variables part 2

Lecture 4 Data types part 1

Lecture 5 Data types part 2

Lecture 6 Operators

Lecture 7 If Statements

Lecture 8 Loops

Lecture 9 Functions

Section 2: Pandas

Lecture 10 Pandas

Lecture 11 Pandas 2

Lecture 12 Pandas 3

Section 3: Feature Engineering

Lecture 13 Feature Scaling

Lecture 14 Feature Scaling in Python

Lecture 15 Label Encoding

Lecture 16 One Hot Encoding

Lecture 17 Outlier Detection

Section 4: Evaluation of the model performances

Lecture 18 Train-Test Split

Lecture 19 MSE - RMSE

Lecture 20 Confusion Matrix - Accuracy Score

Section 5: Machine Learning - Supervised vs Unsupervised

Lecture 21 Supervised vs Unsupervised Machine Learning

Section 6: Data set we are going to use in regression tasks

Lecture 22 EDA

Lecture 23 Feature Engineering

Section 7: Data set we are going to use in classification algorithms

Lecture 24 EDA

Lecture 25 Feature Engineering

Section 8: Linear Regression

Lecture 26 Linear Regression

Lecture 27 Linear Regression 2

Lecture 28 Linear Regression 3

Lecture 29 Linear Regression Coding

Section 9: Logistic Regression

Lecture 30 Logistic Regression

Lecture 31 Logistic Regression Coding

Section 10: K Nearest Neighbors

Lecture 32 K Nearest Neighbors

Lecture 33 K-Nearest Neighbors Coding (Elbow Method)

Lecture 34 K-Nearest Neighbors Coding

Section 11: Support Vector Machines

Lecture 35 Support Vector Machines

Lecture 36 Support Vector Regression Coding

Section 12: Decision Tree

Lecture 37 Decision Tree

Section 13: Random Forest

Lecture 38 Random Forest

Lecture 39 Random Forest Regression

Section 14: Finding the best performing algorithm

Lecture 40 About this section

Lecture 41 For regression data

Lecture 42 For classification data

Lecture 43 Classification part 2

Section 15: K-means Clustering

Lecture 44 K-means Clustering

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