| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction to Machine Learning | |||
| 1 - Introduction to Machine Learning English.vtt | 6.7 KB | ||
| 1 - Introduction to Machine Learning.mp4 | 30.6 MB | ||
| 10 - Support Vector Machines | |||
| 11 - K Nearest Neighbors for Classification and Regression | |||
| 12 - Decision Tree Classifier Algorithm | |||
| 13 - Random Forest Classifier Algorithm | |||
| 14 - Naive Bayes Algorithm | |||
| 15 - Resources | |||
| 64 - Download all the notebooks and datasets here.html | 102.4 B | ||
| Notebooks | |||
| 16 - KMeans Clustering | |||
| 2 - Types of Machine Learning | |||
| 3 - The Machine Learning Pipeline | |||
| 10 - 10 Introduction to iPython Environment English.vtt | 10.4 KB | ||
| 10 - 10 Introduction to iPython Environment.mp4 | 51.6 MB | ||
| 11 - Important Libraries in Python.html | 307.2 B | ||
| 4 - Numpy Library | |||
| 12 - 11Creating a numpy array English.vtt | 17 KB | ||
| 12 - 11Creating a numpy array.mp4 | 49.6 MB | ||
| 13 - 12 Processing the numpy arrays English.vtt | 16.5 KB | ||
| 13 - 12 Processing the numpy arrays.mp4 | 58.2 MB | ||
| 14 - 13 Accessing Columns from Numpy Matrices English.vtt | 5 KB | ||
| 14 - 13 Accessing Columns from Numpy Matrices.mp4 | 18.6 MB | ||
| 15 - 14 Statistical methods in Numpy English.vtt | 14.7 KB | ||
| 15 - 14 Statistical methods in Numpy.mp4 | 56.2 MB | ||
| 16 - 15 Matrix Operations in Numpy English.vtt | 13.6 KB | ||
| 16 - 15 Matrix Operations in Numpy.mp4 | 53.5 MB | ||
| 17 - 16 Iterating through the numpy array English.vtt | 5.9 KB | ||
| 17 - 16 Iterating through the numpy array.mp4 | 24.5 MB | ||
| 5 - Pandas Library | |||
| 18 - 17 An Intuition on Pandas Dataframe and Series English.vtt | 6.5 KB | ||
| 18 - 17 An Intuition on Pandas Dataframe and Series.mp4 | 28.1 MB | ||
| 19 - 18 Using numpy arrays to create Pandas Series English.vtt | 8.1 KB | ||
| 19 - 18 Using numpy arrays to create Pandas Series.mp4 | 28.8 MB | ||
| 20 - 19 Using dictionary to create Pandas Series English.vtt | 7.2 KB | ||
| 20 - 19 Using dictionary to create Pandas Series.mp4 | 26.4 MB | ||
| 21 - 20 Using a scalar to create Pandas Series English.vtt | 2.1 KB | ||
| 21 - 20 Using a scalar to create Pandas Series.mp4 | 10.5 MB | ||
| 22 - 21 Series Processing English.vtt | 1.5 KB | ||
| 22 - 21 Series Processing.mp4 | 8.5 MB | ||
| 23 - 22 Creating Pandas Dataframe from series English.vtt | 7.1 KB | ||
| 23 - 22 Creating Pandas Dataframe from series.mp4 | 24.5 MB | ||
| 24 - 23 Using lists of data to create a Pandas Dataframe English.vtt | 5 KB | ||
| 24 - 23 Using lists of data to create a Pandas Dataframe.mp4 | 21 MB | ||
| 25 - 24 Another approach to create Dataframes English.vtt | 4.2 KB | ||
| 25 - 24 Another approach to create Dataframes.mp4 | 21.6 MB | ||
| 26 - 25 Directly creating a pandas dataframe from numpy arrays English.vtt | 1.8 KB | ||
| 26 - 25 Directly creating a pandas dataframe from numpy arrays.mp4 | 8.2 MB | ||
| 6 - Analysis of Datasets using Pandas and Matplotlib Library | |||
| 27 - 26 Loading the dataset Important English.vtt | 6.7 KB | ||
| 27 - 26 Loading the dataset Important.mp4 | 32.3 MB | ||
| 28 - 27 Analysis of Datasets I English.vtt | 7.3 KB | ||
| 28 - 27 Analysis of Datasets I.mp4 | 42 MB | ||
| 29 - 28 Analysis of Datasets by Plotting II English.vtt | 17.1 KB | ||
| 29 - 28 Analysis of Datasets by Plotting II.mp4 | 71.1 MB | ||
| 7 - The Scikitlearn Library and Preprocessing Techniques | |||
| 30 - 29 Working with Iris Dataset from sklearn English.vtt | 32.1 KB | ||
| 30 - 29 Working with Iris Dataset from sklearn.mp4 | 194.1 MB | ||
| 31 - 30 Binarization English.vtt | 9.6 KB | ||
| 31 - 30 Binarization.mp4 | 43.2 MB | ||
| 32 - 31 Feature Scaling English.vtt | 9.4 KB | ||
| 32 - 31 Feature Scaling.mp4 | 46.9 MB | ||
| 8 - Supervised Learning Linear Regression | |||
| 33 - 32 Analysis of Linear Regression English.vtt | 21.3 KB | ||
| 33 - 32 Analysis of Linear Regression.mp4 | 73.8 MB | ||
| 34 - Use of Gradient Descent Optimizer English.vtt | 10.7 KB | ||
| 34 - Use of Gradient Descent Optimizer.mp4 | 33 MB | ||
| 35 - The Gradient Descent Optimizer Algorithm English.vtt | 27.6 KB | ||
| 35 - The Gradient Descent Optimizer Algorithm.mp4 | 81.1 MB | ||
| 36 - 33 Demand vs Price Problem to understand Linear Regression English.vtt | 11.6 KB | ||
| 36 - 33 Demand vs Price Problem to understand Linear Regression.mp4 | 71.3 MB | ||
| 37 - 34 Implementation of Linear Regression I English.vtt | 12.7 KB | ||
| 37 - 34 Implementation of Linear Regression I.mp4 | 75.8 MB | ||
| 38 - 35 Implementation of Linear Regression II English.vtt | 6.9 KB | ||
| 38 - 35 Implementation of Linear Regression II.mp4 | 48.2 MB | ||
| 39 - 36 Visualizing the LBF using matplotlib English.vtt | 4.8 KB | ||
| 39 - 36 Visualizing the LBF using matplotlib.mp4 | 23.3 MB | ||
| 9 - Logistic Regression for Classification Problems | |||
| 40 - 37 Why does Linear Regression fail for a classification problem English.vtt | 11.2 KB | ||
| 40 - 37 Why does Linear Regression fail for a classification problem.mp4 | 39.1 MB | ||
| 41 - 38 The Sigmoid function in Logistic Regression English.vtt | 6.3 KB | ||
| 41 - 38 The Sigmoid function in Logistic Regression.mp4 | 24 MB | ||
| 42 - 39 The Confusion Matrix English.vtt | 14.2 KB | ||
| 42 - 39 The Confusion Matrix.mp4 | 60.1 MB | ||
| 43 - 40 Implementation of Logistic Regression I English.vtt | 20.2 KB | ||
| 43 - 40 Implementation of Logistic Regression I.mp4 | 150.3 MB | ||
| 44 - 41 Creating an heatmap of the confusion matrix English.vtt | 4.1 KB | ||
| 44 - 41 Creating an heatmap of the confusion matrix.mp4 | 22.1 MB | ||
| 6 - 6 The Machine Learning Pipeline Data Collection English.vtt | 9.4 KB | ||
| 6 - 6 The Machine Learning Pipeline Data Collection.mp4 | 43.8 MB | ||
| 7 - 7 Importance of Data Prepocessing English.vtt | 3.4 KB | ||
| 7 - 7 Importance of Data Prepocessing.mp4 | 17.9 MB | ||
| 8 - 8 Importance of Feature Selection and Feature Engineering English.vtt | 10.8 KB | ||
| 8 - 8 Importance of Feature Selection and Feature Engineering.mp4 | 52.5 MB | ||
| 9 - 9 The Machine Learning Terminologies English.vtt | 7.5 KB | ||
| 9 - 9 The Machine Learning Terminologies.mp4 | 36.5 MB | ||
| 4 - 4 Difference between Supervised and Unsupervised Learning English.vtt | 9.1 KB | ||
| 4 - 4 Difference between Supervised and Unsupervised Learning.mp4 | 39.2 MB | ||
| 5 - 5 Algorithms in Supervised and Unsupervised Learning English.vtt | 3.8 KB | ||
| 5 - 5 Algorithms in Supervised and Unsupervised Learning.mp4 | 12.9 MB | ||
| 65 - The complete flow of KMeans Clustering English.vtt | 19.3 KB | ||
| 65 - The complete flow of KMeans Clustering.mp4 | 89 MB | ||
| 66 - The concept of Overfitting and Underfitting English.vtt | 18.1 KB | ||
| 66 - The concept of Overfitting and Underfitting.mp4 | 78.9 MB | ||
| Decision Tree.ipynb | 35.6 KB | ||
| KNN Regression.ipynb | 35.8 KB | ||
| KNN.ipynb | 4.5 KB | ||
| LinearRegression.ipynb | 38.1 KB | ||
| LogisticRegression.ipynb | 14.9 KB | ||
| MyFirstNotebook.ipynb | 16.9 KB | ||
| Naive Bayes' Algorithm.ipynb | 9 KB | ||
| Naive Bayes' Classifier.ipynb | 26.3 KB | ||
| Preprocessing Techniques.ipynb | 2.5 KB | ||
| Product.csv | 10.7 KB | ||
| Random Forest Classifier.ipynb | 9 KB | ||
| SVM.ipynb | 8.3 KB | ||
| Salary_Data.csv | 409.6 B | ||
| car.csv | 10.3 KB | ||
| 62 - 59 Naive Bayes Classifier English.vtt | 13.6 KB | ||
| 62 - 59 Naive Bayes Classifier.mp4 | 61.4 MB | ||
| 63 - 60 Implementing Naive Bayes Classifier for wine dataset English.vtt | 16.9 KB | ||
| 63 - 60 Implementing Naive Bayes Classifier for wine dataset.mp4 | 141.7 MB | ||
| 60 - 56 Ensemble Techniques Random Forest Classifier English.vtt | 8.9 KB | ||
| 60 - 56 Ensemble Techniques Random Forest Classifier.mp4 | 41.4 MB | ||
| 61 - 57 Implementing Random Classifier in Python English.vtt | 7.2 KB | ||
| 61 - 57 Implementing Random Classifier in Python.mp4 | 54.3 MB | ||
| 54 - 51 Introduction to Decision Trees English.vtt | 921.6 B | ||
| 54 - 51 Introduction to Decision Trees.mp4 | 1.9 MB | ||
| 55 - 52 Basic Tree Terminologies English.vtt | 18.8 KB | ||
| 55 - 52 Basic Tree Terminologies.mp4 | 90 MB | ||
| 56 - 53 Example 1 for Decision Tree English.vtt | 3.4 KB | ||
| 56 - 53 Example 1 for Decision Tree.mp4 | 14.3 MB | ||
| 57 - 531 Example 2 for Decision Tree English.vtt | 2.6 KB | ||
| 57 - 531 Example 2 for Decision Tree.mp4 | 8.8 MB | ||
| 58 - 54 Implementation of Decision Tree Algorithm I English.vtt | 3.5 KB | ||
| 58 - 54 Implementation of Decision Tree Algorithm I.mp4 | 25.5 MB | ||
| 59 - 55 Implementation of Decision Tree Algorithm II English.vtt | 2.7 KB | ||
| 59 - 55 Implementation of Decision Tree Algorithm II.mp4 | 22.5 MB | ||
| 48 - 45 Drawing the classification diagrams English.vtt | 7.3 KB | ||
| 48 - 45 Drawing the classification diagrams.mp4 | 30.5 MB | ||
| 49 - 46 Introduction to KNearest Neighbors English.vtt | 3.8 KB | ||
| 49 - 46 Introduction to KNearest Neighbors.mp4 | 16.2 MB | ||
| 50 - 47 Steps in KNN Classification and KNN Regression English.vtt | 8.1 KB | ||
| 50 - 47 Steps in KNN Classification and KNN Regression.mp4 | 35.9 MB | ||
| 51 - 48 Implementing KNN Classification using sklearn English.vtt | 6.7 KB | ||
| 51 - 48 Implementing KNN Classification using sklearn.mp4 | 53.6 MB | ||
| 52 - 49 Implementing KNN Regression Algorithm in Python I English.vtt | 5 KB | ||
| 52 - 49 Implementing KNN Regression Algorithm in Python I.mp4 | 32.5 MB | ||
| 53 - 50 Implementing KNN Regression Algorithm in Python II English.vtt | 2 KB | ||
| 53 - 50 Implementing KNN Regression Algorithm in Python II.mp4 | 18.3 MB | ||
| 45 - 42 Understanding Support Vector Machines and Hyperplanes English.vtt | 13.5 KB | ||
| 45 - 42 Understanding Support Vector Machines and Hyperplanes.mp4 | 52.6 MB | ||
| 46 - 43 Understanding the Kernels of SVM English.vtt | 3 KB | ||
| 46 - 43 Understanding the Kernels of SVM.mp4 | 13.7 MB | ||
| 47 - 44 Implementing Support Vector Classifiers in Python English.vtt | 8.2 KB | ||
| 47 - 44 Implementing Support Vector Classifiers in Python.mp4 | 52.3 MB | ||
| 2 - 2 Features of Machine Learning English.vtt | 3 KB | ||
| 2 - 2 Features of Machine Learning.mp4 | 17.3 MB | ||
| 3 - 3 Traditional Programming vs Machine Learning English.vtt | 7.2 KB | ||
| 3 - 3 Traditional Programming vs Machine Learning.mp4 | 21.9 MB |
Machine Learning Using Python Programming
https://WebToolTip.com
Last updated 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.76 GB | Duration: 8h 3m
Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3
What you'll learn
Machine Learning Algorithms & Terminologies
Artificial Intelligence
Python Libraries - Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn
Requirements
Yes, A Basic Knowledge in Python is preferred
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.2 GB | freecoursewb | 1 week | 10 | 10 | |
| 1.9 GB | freecoursewb | 1 month | 5 | 0 | |
| 2.6 GB | freecoursewb | 1 month | 6 | 3 | |
| 2.5 GB | freecoursewb | 1 month | 1 | 9 | |
| 1.5 GB | freecoursewb | 2 months | 6 | 7 |
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