Udemy - Machine Learning Using Python Programming

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~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)
• 2 - 2 Features of Machine Learning English.vtt (3.0 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)
10 - Support Vector Machines
• 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 (3.0 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)
11 - K Nearest Neighbors for Classification and Regression
• 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 (5.0 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 (2.0 KB)
• 53 - 50 Implementing KNN Regression Algorithm in Python II.mp4 (18.3 MB)
12 - Decision Tree Classifier Algorithm
• 54 - 51 Introduction to Decision Trees English.vtt (0.9 KB)
• 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 (90.0 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)
13 - Random Forest Classifier Algorithm
• 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)
14 - Naive Bayes Algorithm
• 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)
15 - Resources
• 64 - Download all the notebooks and datasets here.html (0.1 KB)
Notebooks
• 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 (9.0 KB)
• Naive Bayes' Classifier.ipynb (26.3 KB)
• Preprocessing Techniques.ipynb (2.5 KB)
• Product.csv (10.7 KB)
• Random Forest Classifier.ipynb (9.0 KB)
• SVM.ipynb (8.3 KB)
• Salary_Data.csv (0.4 KB)
• car.csv (10.3 KB)
16 - KMeans Clustering
• 65 - The complete flow of KMeans Clustering English.vtt (19.3 KB)
• 65 - The complete flow of KMeans Clustering.mp4 (89.0 MB)
• 66 - The concept of Overfitting and Underfitting English.vtt (18.1 KB)
• 66 - The concept of Overfitting and Underfitting.mp4 (78.9 MB)
2 - Types of Machine Learning
• 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)
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 (0.3 KB)
• 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 - Numpy Library
• 12 - 11Creating a numpy array English.vtt (17.0 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 (5.0 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 (5.0 KB)
• 24 - 23 Using lists of data to create a Pandas Dataframe.mp4 (21.0 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 (42.0 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 (33.0 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 (24.0 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)
• Bonus Resources.txt (0.1 KB)