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| 1.1 python-for-ml-and-ds-projects.zip |
272.74Мб |
| 1. Arithmatic Operations in Python.mp4 |
40.77Мб |
| 1. Boosting Algorithms Introduction.mp4 |
55.50Мб |
| 1. Course Introduction.mp4 |
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| 1. Cross Validation Regularization and Hyperparameter Optimization Introduction.mp4 |
28.35Мб |
| 1. DBSCAN Introduction.mp4 |
46.82Мб |
| 1. Decision Tree Introduction.mp4 |
34.27Мб |
| 1. Ensemble Learning Bagging and Boosting Introduction.mp4 |
37.25Мб |
| 1. Hierarchical Clustering Introduction.mp4 |
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| 1. Introduction.mp4 |
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| 1. Introduction.srt |
3.91Кб |
| 1. Introduction to NLP.mp4 |
22.55Мб |
| 1. Introduction to Plotly and Cufflinks.mp4 |
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| 1. Introduction to Unsupervised Learning.mp4 |
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| 1. IRIS Dataset Introduction.mp4 |
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| 1. KNN Introduction.mp4 |
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| 1. Linear Regression Introduction.mp4 |
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| 1. Logistic Regression Introduction.mp4 |
20.24Мб |
| 1. Matplotlib Introduction.mp4 |
32.00Мб |
| 1. Numpy Introduction - Create Numpy Array.mp4 |
35.91Мб |
| 1. Pandas Series Introduction Part 1.mp4 |
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| 1. PCA Introduction.mp4 |
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| 1. SVM Introduction.mp4 |
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| 1. What is Neuron.mp4 |
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| 10. 10 Set.mp4 |
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| 10. Arithmetic Operations.mp4 |
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| 10. CatBoost Hyperparameter Optimization.mp4 |
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| 10. cat plot.mp4 |
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| 10. Classification Comparison with and without PCA.mp4 |
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| 10. Clusters Visualization.mp4 |
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| 10. Concatenation and Sorting.mp4 |
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| 10. Customer Churn Dataset Loading.mp4 |
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| 10. Data Types Correction and Mapping.mp4 |
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| 10. Diabetes Dataset Loading.mp4 |
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| 10. Exploratory Data Analysis- Pair Plot.mp4 |
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| 10. Hexbin Plot.mp4 |
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| 10. K-Fold and LeaveOneOut Cross Validation.mp4 |
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| 10. Linear SVM Model on Scaled Feature.mp4 |
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| 10. Pair Plot.mp4 |
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| 10. Subplot Part 2.mp4 |
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| 11. Box Plot.mp4 |
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| 11. Data Visualization Part 1.mp4 |
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| 11. Decision Boundary Visualization.mp4 |
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| 11. Decision Tree Regression.mp4 |
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| 11. Dictionary.mp4 |
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| 11. Exploratory Data Analysis- Hist Plot.mp4 |
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| 11. Grid Search Hypyerparameter Tuning.mp4 |
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| 11. NULL Values Handling.mp4 |
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| 11. One-Hot Encoding.mp4 |
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| 11. Pie Chart.mp4 |
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| 11. Polynomial, Sigmoid, RBF Kernels in SVM.mp4 |
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| 11. Subplots.mp4 |
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| 11. Train Test Split.mp4 |
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| 12. Boxen Plot.mp4 |
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| 12. Conditional Statements - If Else.mp4 |
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| 12. Creating a Zoomed Sub-Figure of a Figure.mp4 |
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| 12. DataFrame Data Filtering Part 1.mp4 |
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| 12. Data Visualization Part 2.mp4 |
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| 12. Exploratory Data Analysis- Heatmap.mp4 |
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| 12. Putting Everything Together.mp4 |
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| 12. Random Grid Search Hyperparameter Tuning.mp4 |
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| 12. Scatter Matrix and Subplots.mp4 |
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| 12. TF-IDF Vectorization.mp4 |
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| 13. DataFrame Data Filtering Part 2.mp4 |
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| 13. Data Preprocessing.mp4 |
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| 13. Model Building Training and Evaluation.mp4 |
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| 13. Model Evaluation and Prediction on Real Data.mp4 |
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| 13. Selecting Optimum Number of Clusters.mp4 |
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| 13. Train Test Split and Model Training.mp4 |
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| 13. Violin Plot.mp4 |
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| 13. While Loops.mp4 |
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| 13. xlim and ylim, legend, grid, xticks, yticks.mp4 |
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| 14. 14 Handling Unique and Duplicated Values.mp4 |
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| 14. Bar Plot.mp4 |
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| 14. Clustering for Annual Income vs Spending Score.mp4 |
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| 14. Feature Selection - Recursive Feature Elimination.mp4 |
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| 14. For Loops.mp4 |
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| 14. How to Evaluate the Regression Model Performance.mp4 |
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| 14. Import Neural Networks APIs.mp4 |
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| 14. Model Load and Store.mp4 |
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| 14. Pie Chart and Figure Save.mp4 |
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| 15. 3D Clustering Part 1.mp4 |
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| 15. Accuracy, F1-Score, P, R, AUC_ROC Curve Part 1.mp4 |
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| 15. Functions.mp4 |
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| 15. How to Get Input Shape and Class Weights.mp4 |
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| 15. Plot True House Price vs Predicted Price.mp4 |
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| 16. 3D Clustering Part 2.mp4 |
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| 16. Accuracy, F1-Score, P, R, AUC_ROC Curve Part 2.mp4 |
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| 16. Joint Plot.mp4 |
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| 16. Neural Network Model Building.mp4 |
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| 16. Plotting Learning Curves Part 1.mp4 |
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| 16. Replace Cell Values.mp4 |
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| 16. Working with Date and Time.mp4 |
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| 17. Accuracy, F1-Score, P, R, AUC_ROC Curve Part 3.mp4 |
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| 17. File Handling Read and Write.mp4 |
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| 17. Model Summary Explanation.mp4 |
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| 17. Pair Plot.mp4 |
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| 17. Plotting Learning Curves Part 2.mp4 |
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| 17. Rename, Delete Index and Columns.mp4 |
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| 18. Lambda Apply.mp4 |
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| 18. Machine Learning Model Interpretability- Residuals Plot.mp4 |
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| 18. Model Training.mp4 |
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| 18. Regression Plot.mp4 |
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| 18. ROC Curve and AUC Part 1.mp4 |
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| 19. Controlling Ploted Figure Aesthetics.mp4 |
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| 19. Machine Learning Model Interpretability- Prediction Error Plot.mp4 |
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| 19. Model Evaluation.mp4 |
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| 19. Pandas Groupby.mp4 |
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| 19. ROC Curve and AUC Part 2.mp4 |
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| 2. Array Indexing and Slicing.mp4 |
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| 2. Data Types in Python.mp4 |
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| 2. Generate Dataset.mp4 |
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| 2. Heart-Disease Dataset Understanding.mp4 |
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| 2. How Decision Tree Works.mp4 |
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| 2. How KNN Works.mp4 |
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| 2. How PCA is Done..mp4 |
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| 2. Important Terms in Hierarchical Clustering.mp4 |
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| 2. Introduction to K-Means.mp4 |
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| 2. Load IRIS Dataset.mp4 |
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| 2. Machine Learning Introduction.mp4 |
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| 2. Matplotlib Line Plot Part 1.mp4 |
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| 2. ML Model Training Process.mp4 |
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| 2. Multi-Layer Perceptron.mp4 |
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| 2. Pandas Series Introduction Part 2.mp4 |
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| 2. Plotly Line Plot.mp4 |
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| 2. Random Forest Introduction.mp4 |
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| 2. Regression Examples.mp4 |
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| 2. Scatter Plot.mp4 |
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| 2. Sigmoid Function.mp4 |
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| 2. SVM Kernels.mp4 |
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| 2. What are Key NLP Techniques.mp4 |
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| 20. Groupby Multiple Columns.mp4 |
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| 20. Model Save and Load.mp4 |
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| 20. ROC Curve and AUC Part 3.mp4 |
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| 21. Merging, Joining, and Concatenation Part 1.mp4 |
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| 21. Prediction on Real-Life Data.mp4 |
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| 3. Breast Cancer Dataset Introduction.mp4 |
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| 3. Dataset Introduction.mp4 |
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| 3. Data Visualization Part 1.mp4 |
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| 3. DBSCAN Clustering.mp4 |
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| 3. Decision Boundary.mp4 |
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| 3. How to Choose Best Number of Clusters.mp4 |
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| 3. Hue, Style and Size Part1.mp4 |
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| 3. IMDB Movie Revenue Line Plot Part 1.mp4 |
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| 3. Install Anaconda and Python on Windows.mp4 |
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| 3. Line Plot.mp4 |
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| 3. MNIST Dataset Loading and Understanding.mp4 |
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| 3. Numpy Data Types.mp4 |
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| 3. Overview of NLP Tools.mp4 |
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| 3. Pandas Series Read From File.mp4 |
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| 3. Scatter Plot.mp4 |
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| 3. Shallow vs Deep Neural Networks.mp4 |
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| 3. Stock Market Data Loading.mp4 |
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| 3. Types of Linear Regression.mp4 |
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| 3. Variable Casting.mp4 |
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| 3. What is Attribute Selection Measures - ASM..mp4 |
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| 4. Activation Function.mp4 |
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| 4. Apply Pythons Built in Functions to Series.mp4 |
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| 4. Assessing the performance of the model.mp4 |
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| 4. Common Challenges in NLP.mp4 |
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| 4. Dataset Loading.mp4 |
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| 4. Dataset Loading.mp4 |
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| 4. Data Visualization.mp4 |
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| 4. Data Visualization.mp4 |
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| 4. Data Visualization.mp4 |
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| 4. Hierarchical Clustering Coding.mp4 |
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| 4. Hue, Style and Size Part2.mp4 |
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| 4. IMDB Movie Revenue Line Plot Part 2.mp4 |
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| 4. Install Anaconda in Linux.mp4 |
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| 4. K-Means Clustering with Scikit-Learn.mp4 |
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| 4. np.nan and np.inf.mp4 |
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| 4. PCA Applications.mp4 |
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| 4. Secondary Axis.mp4 |
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| 4. Spectral Clustering.mp4 |
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| 4. Stacked Bar Plot.mp4 |
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| 4. Strings Operation in Python.mp4 |
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| 4. Titanic Dataset Introduction.mp4 |
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| 4. Train Test Split.mp4 |
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| 5. AdaBoost Model Training.mp4 |
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| 5. Application of Unsupervised Learning.mp4 |
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| 5. apply() for Pandas Series.mp4 |
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| 5. Bag of Words - The Simples Word Embedding Technique.mp4 |
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| 5. Bar and Barh Plot.mp4 |
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| 5. Bias-Variance tradeoff.mp4 |
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| 5. Box and Area Plot.mp4 |
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| 5. Cancer Data Visualization Part 1.mp4 |
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| 5. Jupyter Notebook Introduction and Keyboard Shortcuts.mp4 |
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| 5. Line Plot Part 1.mp4 |
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| 5. Line Plot Rank vs Runtime Votes Metascore.mp4 |
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| 5. PCA Coding.mp4 |
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| 5. Spectral Clustering Coding.mp4 |
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| 5. Train Test Split and One-Hot Encoding.mp4 |
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| 5. Train Test Split and Standardization.mp4 |
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| 6. AdaBoost Hyperparameter Tuning.mp4 |
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| 6. Cancer Data Visualization Part 2.mp4 |
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| 6. Customers Data Loading.mp4 |
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| 6. EDA - Heatmap and Density Plot.mp4 |
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| 6. KNN Model Building and Training.mp4 |
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| 6. Linear Regression and SVM Model Training.mp4 |
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| 6. Line Plot Part 2.mp4 |
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| 6. Line Styling and Putting Labels.mp4 |
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| 6. Optimizers in Deep Learning.mp4 |
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| 6. Pandas DataFrame Creation from Scratch.mp4 |
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| 6. PCA Compression Analysis.mp4 |
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| 6. Random Forest Classifier Training and Evaluation.mp4 |
59.50Мб |
| 6. Shape(), Reshape(), Ravel(), Flatten().mp4 |
20.53Мб |
| 6. Stacked Bar Plot.mp4 |
50.94Мб |
| 6. String Formatting and Modification.mp4 |
29.85Мб |
| 6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4 |
20.01Мб |
| 6. Train Test Split.mp4 |
20.36Мб |
| 6. What is sklearn and train-test-split.mp4 |
39.68Мб |
| 60 |
1.45Мб |
| 61 |
221.96Кб |
| 62 |
509.55Кб |
| 63 |
695.72Кб |
| 64 |
704.87Кб |
| 65 |
1012.75Кб |
| 66 |
1.33Мб |
| 67 |
1.82Мб |
| 68 |
632.82Кб |
| 69 |
649.52Кб |
| 7 |
1.05Мб |
| 7. arange(), linspace(), range(), random(), zeros(), and ones().mp4 |
55.01Мб |
| 7. Boolean Variables and Evaluation.mp4 |
15.43Мб |
| 7. Data Loading for Random Forest Regression.mp4 |
66.64Мб |
| 7. Data Reconstruction.mp4 |
104.85Мб |
| 7. Data Standardization.mp4 |
45.47Мб |
| 7. Data Visualization.mp4 |
76.06Мб |
| 7. Histogram.mp4 |
78.37Мб |
| 7. Hist Plot, Bubble Plot and Heatmap.mp4 |
78.69Мб |
| 7. Hyperparameter Tuning.mp4 |
53.95Мб |
| 7. Line Plot Part 3.mp4 |
42.31Мб |
| 7. Load Spam Dataset.mp4 |
18.57Мб |
| 7. Missing Age Imputation Part 1.mp4 |
52.05Мб |
| 7. Model Training and Evaluation.mp4 |
27.20Мб |
| 7. Python Package Upgrade and Import.mp4 |
36.04Мб |
| 7. Read Files as DataFrame.mp4 |
56.15Мб |
| 7. Regularization Introduction.mp4 |
56.61Мб |
| 7. Scatter, Bar, and Histogram Plot Part 1.mp4 |
53.32Мб |
| 7. Steps to Build Neural Network.mp4 |
64.09Мб |
| 7. XGBoost Introduction.mp4 |
29.70Мб |
| 70 |
1.10Мб |
| 71 |
1.36Мб |
| 72 |
1.39Мб |
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1.42Мб |
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1.58Мб |
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1.70Мб |
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1.77Мб |
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1.78Мб |
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1.85Мб |
| 79 |
27.75Кб |
| 8 |
1.62Мб |
| 8. Box Plot.mp4 |
44.30Мб |
| 8. Choosing Right Number of the Principle Components.mp4 |
56.42Мб |
| 8. Columns Manipulation Part 1.mp4 |
45.45Мб |
| 8. Install TensorfFlow in Windows.mp4 |
67.97Мб |
| 8. K-Means Clustering Data Preparation.mp4 |
55.23Мб |
| 8. List in Python.mp4 |
37.55Мб |
| 8. Load Boston Housing Dataset.mp4 |
32.74Мб |
| 8. Manual Hyperparameter Adjustment.mp4 |
74.24Мб |
| 8. Missing Age Imputation Part 2.mp4 |
90.38Мб |
| 8. Pros and Cons of KNN.mp4 |
10.78Мб |
| 8. Random Forest Regression Model Building.mp4 |
19.57Мб |
| 8. Scatter, Bar, and Histogram Plot Part 2.mp4 |
66.37Мб |
| 8. Subplots.mp4 |
31.67Мб |
| 8. Text Preprocessing.mp4 |
45.87Мб |
| 8. Train Test Split.mp4 |
37.18Мб |
| 8. Tree Visualization.mp4 |
36.17Мб |
| 8. Where.mp4 |
28.55Мб |
| 8. XGBoost Model Training and Hyperparameter Tuning.mp4 |
63.96Мб |
| 80 |
193.56Кб |
| 81 |
505.19Кб |
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515.98Кб |
| 83 |
785.92Кб |
| 84 |
868.54Кб |
| 85 |
1014.39Кб |
| 86 |
1.55Мб |
| 87 |
1.80Мб |
| 88 |
55.63Кб |
| 89 |
166.67Кб |
| 9 |
1.79Мб |
| 9. Area and Scatter Plot.mp4 |
74.68Мб |
| 9. CatBoost Model Training.mp4 |
39.92Мб |
| 9. Columns Manipulation Part 2.mp4 |
47.52Мб |
| 9. Data Reconstruction with 95% Information.mp4 |
34.11Мб |
| 9. Dataset Analysis.mp4 |
52.50Мб |
| 9. Feature Engineering.mp4 |
33.71Мб |
| 9. Hyperparameter Optimization.mp4 |
36.81Мб |
| 9. Hyperparameter Optimization.mp4 |
33.56Мб |
| 9. Imputation of Missing Embark Town.mp4 |
67.08Мб |
| 9. Install TensorFlow in Linux.mp4 |
69.46Мб |
| 9. K-Means Clustering for Age and Spending Score.mp4 |
40.35Мб |
| 9. Linear SVM Model Building and Training.mp4 |
76.09Мб |
| 9. Numpy Array Read and Write.mp4 |
50.46Мб |
| 9. sns.lineplot() and sns.scatterplot().mp4 |
28.01Мб |
| 9. Subplot Part 1.mp4 |
58.67Мб |
| 9. Tuple in Python.mp4 |
27.75Мб |
| 9. Types of Cross Validation.mp4 |
42.03Мб |
| 90 |
698.58Кб |
| 91 |
1.13Мб |
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1.44Мб |
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1.50Мб |
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1.95Мб |
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1.96Мб |
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160.38Кб |
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218.73Кб |
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252.37Кб |
| 99 |
722.60Кб |
| TutsNode.net.txt |
63б |