\n- Break Up Dates And Times Into Multiple Features<\/li>\n
- Calculate Difference Between Dates And Times<\/li>\n
- Convert Strings To Dates<\/li>\n
- Convert pandas Columns Time Zone<\/li>\n
- Encode Days Of The Week<\/li>\n
- Handling Missing Values In Time Series<\/li>\n
- Handling Time Zones<\/li>\n
- Lag A Time Feature<\/li>\n
- Rolling Time Window<\/li>\n
- Select Date And Time Ranges<\/li>\n<\/ul>\n
Feature Engineering<\/p>\n
\n- Dimensionality Reduction On Sparse Feature Matrix<\/li>\n
- Dimensionality Reduction With Kernel PCA<\/li>\n
- Dimensionality Reduction With PCA<\/li>\n
- Feature Extraction With PCA<\/li>\n
- Group Observations Using K-Means Clustering<\/li>\n
- Selecting The Best Number Of Components For LDA<\/li>\n
- Selecting The Best Number Of Components For TSVD<\/li>\n
- Using Linear Discriminant Analysis For Dimensionality Reduction<\/li>\n<\/ul>\n
Feature Selection<\/p>\n
\n- ANOVA F-value For Feature Selection<\/a><\/li>\n
- Chi-Squared For Feature Selection<\/a><\/li>\n
- Drop Highly Correlated Features<\/a><\/li>\n
- Recursive Feature Elimination<\/a><\/li>\n
- Variance Thresholding Binary Features<\/a><\/li>\n
- Variance Thresholding For Feature Selection<\/a><\/li>\n<\/ul>\n
Model Evaluation<\/p>\n
\n- Accuracy<\/a><\/li>\n
- Create Baseline Classification Model<\/a><\/li>\n
- Create Baseline Regression Model<\/a><\/li>\n
- Cross Validation Pipeline<\/a><\/li>\n
- Cross Validation With Parameter Tuning Using Grid Search<\/a><\/li>\n
- Cross-Validation<\/a><\/li>\n
- Custom Performance Metric<\/a><\/li>\n
- F1 Score<\/a><\/li>\n
- Generate Text Reports On Performance<\/a><\/li>\n
- Nested Cross Validation<\/a><\/li>\n
- Plot The Learning Curve<\/a><\/li>\n
- Plot The Receiving Operating Characteristic Curve<\/a><\/li>\n
- Plot The Validation Curve<\/a><\/li>\n
- Precision<\/a><\/li>\n
- Recall<\/a><\/li>\n
- Split Data Into Training And Test Sets<\/a><\/li>\n<\/ul>\n
Model Selection<\/p>\n
\n- Find Best Preprocessing Steps During Model Selection<\/a><\/li>\n
- Hyperparameter Tuning Using Grid Search<\/a><\/li>\n
- Hyperparameter Tuning Using Random Search<\/a><\/li>\n
- Model Selection Using Grid Search<\/a><\/li>\n
- Pipelines With Parameter Optimization<\/a><\/li>\n<\/ul>\n
Linear Regression<\/p>\n
\n- Adding Interaction Terms<\/a><\/li>\n
- Create Interaction Features<\/a><\/li>\n
- Effect Of Alpha On Lasso Regression<\/a><\/li>\n
- Lasso Regression<\/a><\/li>\n
- Linear Regression<\/a><\/li>\n
- Linear Regression Using Scikit-Learn<\/a><\/li>\n
- Ridge Regression<\/a><\/li>\n
- Selecting The Best Alpha Value In Ridge Regression<\/a><\/li>\n<\/ul>\n
Logistic Regression<\/p>\n
\n- Fast C Hyperparameter Tuning<\/a><\/li>\n
- Handling Imbalanced Classes In Logistic Regression<\/a><\/li>\n
- Logistic Regression<\/a><\/li>\n
- Logistic Regression On Very Large Data<\/a><\/li>\n
- Logistic Regression With L1 Regularization<\/a><\/li>\n
- One Vs. Rest Logistic Regression<\/a><\/li>\n<\/ul>\n
Trees And Forests<\/p>\n
\n- Adaboost Classifier<\/a><\/li>\n
- Decision Tree Classifier<\/a><\/li>\n
- Decision Tree Regression<\/a><\/li>\n
- Feature Importance<\/a><\/li>\n
- Feature Selection Using Random Forest<\/a><\/li>\n
- Handle Imbalanced Classes In Random Forest<\/a><\/li>\n
- Random Forest Classifier<\/a><\/li>\n
- Random Forest Classifier Example<\/a><\/li>\n
- Random Forest Regression<\/a><\/li>\n
- Select Important Features In Random Forest<\/a><\/li>\n
- Titanic Competition With Random Forest<\/a><\/li>\n
- Visualize A Decision Tree<\/a><\/li>\n<\/ul>\n
Nearest Neighbors<\/p>\n
\n- Identifying Best Value Of k<\/a><\/li>\n
- K-Nearest Neighbors Classification<\/a><\/li>\n
- Radius-Based Nearest Neighbor Classifier<\/a><\/li>\n<\/ul>\n
Support Vector Machines<\/p>\n
\n- Calibrate Predicted Probabilities In SVC<\/a><\/li>\n
- Find Nearest Neighbors<\/a><\/li>\n
- Find Support Vectors<\/a><\/li>\n
- Imbalanced Classes In SVM<\/a><\/li>\n
- Plot The Support Vector Classifiers Hyperplane<\/a><\/li>\n
- SVC Parameters When Using RBF Kernel<\/a><\/li>\n
- Support Vector Classifier<\/a><\/li>\n<\/ul>\n
Naive Bayes<\/p>\n
\n- Bernoulli Naive Bayes Classifier<\/a><\/li>\n
- Calibrate Predicted Probabilities<\/a><\/li>\n
- Gaussian Naive Bayes Classifier<\/a><\/li>\n
- Multinomial Logistic Regression<\/a><\/li>\n
- Multinomial Naive Bayes Classifier<\/a><\/li>\n
- Naive Bayes Classifier From Scratch<\/a><\/li>\n<\/ul>\n
Clustering<\/p>\n
\n- Agglomerative Clustering<\/a><\/li>\n
- DBSCAN Clustering<\/a><\/li>\n
- Evaluating Clustering<\/a><\/li>\n
- Meanshift Clustering<\/a><\/li>\n
- Mini-Batch k-Means Clustering<\/a><\/li>\n
- k-Means Clustering<\/a><\/li>\n<\/ul>\n
Deep Learning<\/strong><\/p>\nKeras<\/p>\n
\n- Adding Dropout<\/a><\/li>\n
- Convolutional Neural Network<\/a><\/li>\n
- Feedforward Neural Network For Binary Classification<\/a><\/li>\n
- Feedforward Neural Network For Multiclass Classification<\/a><\/li>\n
- Feedforward Neural Networks For Regression<\/a><\/li>\n
- LSTM Recurrent Neural Network<\/a><\/li>\n
- Neural Network Early Stopping<\/a><\/li>\n
- Neural Network Weight Regularization<\/a><\/li>\n
- Preprocessing Data For Neural Networks<\/a><\/li>\n
- Save Model Training Progress<\/a><\/li>\n
- Tuning Neural Network Hyperparameters<\/a><\/li>\n
- Visualize Loss History<\/a><\/li>\n
- Visualize Neural Network Architecutre<\/a><\/li>\n
- Visualize Performance History<\/a><\/li>\n
- k-Fold Cross-Validating Neural Networks<\/a><\/li>\n<\/ul>\n
Data Wrangling<\/p>\n
\n- Apply Functions By Group In Pandas<\/li>\n
- Apply Operations To Groups In Pandas<\/li>\n
- Applying Operations Over pandas Dataframes<\/li>\n
- Assign A New Column To A Pandas DataFrame<\/li>\n
- Break A List Into N-Sized Chunks<\/li>\n
- Breaking Up A String Into Columns Using Regex In pandas<\/li>\n
- Columns Shared By Two Data Frames<\/li>\n
- Construct A Dictionary From Multiple Lists<\/li>\n
- Convert A CSV Into Python Code To Recreate It<\/li>\n
- Convert A Categorical Variable Into Dummy Variables<\/li>\n
- Convert A Categorical Variable Into Dummy Variables<\/li>\n
- Convert A String Categorical Variable To A Numeric Variable<\/li>\n
- Convert A Variable To A Time Variable In pandas<\/li>\n
- Count Values In Pandas Dataframe<\/li>\n
- Create A Pipeline In Pandas<\/li>\n
- Create A pandas Column With A For Loop<\/li>\n
- Create Counts Of Items<\/li>\n
- Create a Column Based on a Conditional in pandas<\/li>\n
- Creating Lists From Dictionary Keys And Values<\/li>\n
- Crosstabs In pandas<\/li>\n
- Delete Duplicates In pandas<\/li>\n
- Descriptive Statistics For pandas Dataframe<\/li>\n
- Dropping Rows And Columns In pandas Dataframe<\/li>\n
- Enumerate A List<\/li>\n
- Expand Cells Containing Lists Into Their Own Variables In Pandas<\/li>\n
- Filter pandas Dataframes<\/li>\n
- Find Largest Value In A Dataframe Column<\/li>\n
- Find Unique Values In Pandas Dataframes<\/li>\n
- Geocoding And Reverse Geocoding<\/li>\n
- Geolocate A City And Country<\/li>\n
- Geolocate A City Or Country<\/li>\n
- Group A Time Series With pandas<\/li>\n
- Group Data By Time<\/li>\n
- Group Pandas Data By Hour Of The Day<\/li>\n
- Grouping Rows In pandas<\/li>\n
- Hierarchical Data In pandas<\/li>\n
- Join And Merge Pandas Dataframe<\/li>\n
- List Unique Values In A pandas Column<\/li>\n
- Load A JSON File Into Pandas<\/li>\n
- Load An Excel File Into Pandas<\/li>\n
- Load Excel Spreadsheet As pandas Dataframe<\/li>\n
- Loading A CSV Into pandas<\/li>\n
- Long To Wide Format<\/li>\n
- Lower Case Column Names In Pandas Dataframe<\/li>\n
- Make New Columns Using Functions<\/li>\n
- Map External Values To Dataframe Values in pandas<\/li>\n
- Missing Data In pandas Dataframes<\/li>\n
- Moving Averages In pandas<\/li>\n
- Normalize A Column In pandas<\/li>\n
- Pivot Tables In pandas<\/li>\n
- Quickly Change A Column Of Strings In Pandas<\/li>\n
- Random Sampling Dataframe<\/li>\n
- Ranking Rows Of Pandas Dataframes<\/li>\n
- Regular Expression Basics<\/li>\n
- Regular Expression By Example<\/li>\n
- Reindexing pandas Series And Dataframes<\/li>\n
- Rename Column Headers In pandas<\/li>\n
- Rename Multiple pandas Dataframe Column Names<\/li>\n
- Replacing Values In pandas<\/li>\n
- Saving A pandas Dataframe As A CSV<\/li>\n
- Search A pandas Column For A Value<\/li>\n
- Select Rows When Columns Contain Certain Values<\/li>\n
- Select Rows With A Certain Value<\/li>\n
- Select Rows With Multiple Filters<\/li>\n
- Selecting pandas DataFrame Rows Based On Conditions<\/li>\n
- Simple Example Dataframes In pandas<\/li>\n
- Sorting Rows In pandas Dataframes<\/li>\n
- Split Lat\/Long Coordinate Variables Into Separate Variables<\/li>\n
- Streaming Data Pipeline<\/li>\n
- String Munging In Dataframe<\/li>\n
- Using List Comprehensions With pandas<\/li>\n
- Using Seaborn To Visualize A pandas Dataframe<\/li>\n
- pandas Data Structures<\/li>\n
- pandas Time Series Basics<\/li>\n<\/ul>\n
Data Visualization<\/p>\n
\n- Back To Back Bar Plot In MatPlotLib<\/a><\/li>\n
- Bar Plot In MatPlotLib<\/a><\/li>\n
- Color Palettes in Seaborn<\/a><\/li>\n
- Creating A Time Series Plot With Seaborn And pandas<\/a><\/li>\n
- Creating Scatterplots With Seaborn<\/a><\/li>\n
- Group Bar Plot In MatPlotLib<\/a><\/li>\n
- Histograms In MatPlotLib<\/a><\/li>\n
- Making A Matplotlib Scatterplot From A Pandas Dataframe<\/a><\/li>\n
- Matplotlib, A Simple Example<\/a><\/li>\n
- Pie Chart In MatPlotLib<\/a><\/li>\n
- Scatterplot In MatPlotLib<\/a><\/li>\n
- Stacked Percentage Bar Plot In MatPlotLib<\/a><\/li>\n<\/ul>\n<\/div>\n
Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Author: ajit jaokar Here is another resource I use for teaching my students at AI for Edge computing course I like this resource because I […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":465,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2983"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=2983"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2983\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/473"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=2983"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=2983"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=2983"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}