Udemy - Python for Machine Learning - The Complete Beginner's Course

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[ DevCourseWeb.com ] Udemy - Python for Machine Learning - The Complete Beginner's Course
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Introduction to Machine Learning
    • 1. What is Machine Learning.mp4 (7.5 MB)
    • 1. What is Machine Learning.srt (2.1 KB)
    • 2. Applications of Machine Learning.mp4 (6.5 MB)
    • 2. Applications of Machine Learning.srt (1.9 KB)
    • 3. Machine learning Methods.mp4 (3.7 MB)
    • 3. Machine learning Methods.srt (0.4 KB)
    • 4. What is Supervised learning.mp4 (6.2 MB)
    • 4. What is Supervised learning.srt (1.3 KB)
    • 5. What is Unsupervised learning.mp4 (6.0 MB)
    • 5. What is Unsupervised learning.srt (1.0 KB)
    • 6. Supervised learning vs Unsupervised learning.mp4 (14.3 MB)
    • 6. Supervised learning vs Unsupervised learning.srt (4.4 KB)
    • 7. Course Materials.html (0.1 KB)
    • 7.1 50_Startups.csv (2.4 KB)
    • 7.10 Movie_Id_Titles.original (49.8 KB)
    • 7.11 MultipleLinearRegression.ipynb (8.5 KB)
    • 7.12 Recommender Systems with Python.ipynb (122.4 KB)
    • 7.13 salaries.csv (0.6 KB)
    • 7.14 u.data (2.0 MB)
    • 7.15 user data.csv (10.7 KB)
    • 7.2 Decision_tree.ipynb (14.3 KB)
    • 7.3 homeprices.csv (0.1 KB)
    • 7.4 K-means algorithm numpy&pandas clustering.ipynb (102.3 KB)
    • 7.5 KNN_Binary_Classification.ipynb (25.2 KB)
    • 7.6 linear_regression_houseprice.ipynb (16.3 KB)
    • 7.7 logistic_regression_Binary_Classification.ipynb (2.7 KB)
    • 7.8 mall customers data.csv (4.3 KB)
    • 7.9 mallCustomerData.txt (3.9 KB)
    2. Simple Linear Regression
    • 1. Introduction to regression.mp4 (9.0 MB)
    • 1. Introduction to regression.srt (1.9 KB)
    • 2. How Does Linear Regression Work.mp4 (7.7 MB)
    • 2. How Does Linear Regression Work.srt (1.9 KB)
    • 3. Line representation.mp4 (5.5 MB)
    • 3. Line representation.srt (0.8 KB)
    • 4. Implementation in python Importing libraries & datasets.mp4 (7.6 MB)
    • 4. Implementation in python Importing libraries & datasets.srt (1.4 KB)
    • 5. Implementation in python Distribution of the data.mp4 (9.5 MB)
    • 5. Implementation in python Distribution of the data.srt (2.2 KB)
    • 6. Implementation in python Creating a linear regression object.mp4 (13.2 MB)
    • 6. Implementation in python Creating a linear regression object.srt (2.8 KB)
    3. Multiple Linear Regression
    • 1. Understanding Multiple linear regression.mp4 (6.3 MB)
    • 1. Understanding Multiple linear regression.srt (1.4 KB)
    • 2. Implementation in python Exploring the dataset.mp4 (13.3 MB)
    • 2. Implementation in python Exploring the dataset.srt (3.5 KB)
    • 3. Implementation in python Encoding Categorical Data.mp4 (28.9 MB)
    • 3. Implementation in python Encoding Categorical Data.srt (5.6 KB)
    • 4. Implementation in python Splitting data into Train and Test Sets.mp4 (8.8 MB)
    • 4. Implementation in python Splitting data into Train and Test Sets.srt (1.5 KB)
    • 5. Implementation in python Training the model on the Training set.mp4 (8.6 MB)
    • 5. Implementation in python Training the model on the Training set.srt (1.0 KB)
    • 6. Implementation in python Predicting the Test Set results.mp4 (17.8 MB)
    • 6. Implementation in python Predicting the Test Set results.srt (2.8 KB)
    • 7. Evaluating the performance of the regression model.mp4 (6.0 MB)
    • 7. Evaluating the performance of the regression model.srt (1.3 KB)
    • 8. Root Mean Squared Error in Python.mp4 (11.8 MB)
    • 8. Root Mean Squared Error in Python.srt (2.2 KB)
    4. Classification Algorithms K-Nearest Neighbors
    • 1. Introduction to classification.mp4 (4.7 MB)
    • 1. Introduction to classification.srt (1.1 KB)
    • 10. Implementation in python Results prediction & Confusion matrix.mp4 (9.7 MB)
    • 10. Implementation in python Results prediction & Confusion matrix.srt (1.4 KB)
    • 2. K-Nearest Neighbors algorithm.mp4 (6.1 MB)
    • 2. K-Nearest Neighbors algorithm.srt (0.9 KB)
    • 3. Example of KNN.mp4 (3.5 MB)
    • 3. Example of KNN.srt (0.4 KB)
    • 4. K-Nearest Neighbours (KNN) using python.mp4 (6.1 MB)
    • 4. K-Nearest Neighbours (KNN) using python.srt (1.2 KB)
    • 5. Implementation in python Importing required libraries.mp4 (5.1 MB)
    • 5. Implementation in python Importing required libraries.srt (0.4 KB)
    • 6. Implementation in python Importing the dataset.mp4 (9.3 MB)
    • 6. Implementation in python Importing the dataset.srt (1.3 KB)
    • 7. Implementation in python Splitting data into Train and Test Sets.mp4 (19.7 MB)
    • 7. Implementation in python Splitting data into Train and Test Sets.srt (2.8 KB)
    • 8. Implementation in python Feature Scaling.mp4 (5.7 MB)
    • 8. Implementation in python Feature Scaling.srt (0.3 KB)
    • 9. Implementation in python Importing the KNN classifier.mp4 (12.5 MB)
    • 9. Implementation in python Importing the KNN classifier.srt (2.0 KB)
    5. Classification Algorithms Decision Tree
    • 1. Introduction to decision trees.mp4 (6.5 MB)
    • 1. Introduction to decision trees.srt (1.5 KB)
    • 2. What is Entropy.mp4 (5.2 MB)
    • 2. What is Entropy.srt (1.4 KB)
    • 3. Exploring the dataset.mp4 (6.0 MB)
    • 3. Exploring the dataset.srt (1.3 KB)
    • 4. Decision tree structure.mp4 (6.4 MB)
    • 4. Decision tree structure.srt (1.3 KB)
    • 5. Implementation in python Importing libraries & datasets.mp4 (4.6 MB)
    • 5. Implementation in python Importing libraries & datasets.srt (0.8 KB)
    • 6. Implementation in python Encoding Categorical Data.mp4 (17.0 MB)
    • 6. Implementation in python Encoding Categorical Data.srt (3.4 KB)
    • 7. Implementation in python Splitting data into Train and Test Sets.mp4 (4.9 MB)
    • 7. Implementation in python Splitting data into Train and Test Sets.srt (0.9 KB)
    • 8. Implementation in python Results prediction & Accuracy.mp4 (10.4 MB)
    • 8. Implementation in python Results prediction & Accuracy.srt (2.7 KB)
    6. Classification Algorithms Logistic regression
    • 1. Introduction.mp4 (6.6 MB)
    • 1. Introduction.s

Description

Python for Machine Learning: The Complete Beginner's Course



https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 80 lectures (2h 14m) | Size: 685.4 MB

Learn to create machine learning algorithms in Python for students and professionals

What you'll learn
Learn Python programming and Scikit learn applied to machine learning regression
Understand the underlying theory behind simple and multiple linear regression techniques
Learn to solve regression problems (linear regression and logistic regression)
Learn the theory and the practical implementation of logistic regression using sklearn
Learn the mathematics behind decision trees
Learn about the different algorithms for clustering

Requirements
Experience with the basics of Python
Readiness, flexibility, and passion for learning
Basic mathematical skills



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Udemy - Python for Machine Learning - The Complete Beginner's Course


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685.3 MB
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Udemy - Python for Machine Learning - The Complete Beginner's Course


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