Udemy - Python for Machine Learning: The Complete Beginner's Course [FCS]

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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 Logi

Description

Udemy - Python for Machine Learning: The Complete Beginner's Course [FCS]

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

Created by Meta Brains
Last updated 5/2022
English
English [Auto]


TO GET DIRECT DOWNLOAD LINKS OR GOOGLE DRIVE LINKS VISIT OUR WEBSITE
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685.3 MB
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Udemy - Python for Machine Learning: The Complete Beginner's Course [FCS]


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