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


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