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[ DevCourseWeb com ] Udemy - BigQuery ML - Machine Learning in Google Cloud with BigQuery
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1. Introduction to GCP
1. Introduction to Google Cloud Platform.mp4
1. Introduction to Google Cloud Platform.mp4.jpg
1. Introduction to Google Cloud Platform.srt
2. GCP vs AWS vs Azure - Why choose GCP.mp4
2. GCP vs AWS vs Azure - Why choose GCP.srt
3. AI & ML services in Google Cloud.mp4
3. AI & ML services in Google Cloud.srt
10. Boosted Trees
1. What is Boosting and Why it is needed.mp4
1. What is Boosting and Why it is needed.srt
10. Boosted Tree Example - Hyperparameter Tuning.mp4
10. Boosted Tree Example - Hyperparameter Tuning.srt
10.1 Boosted Tree Hyperparameters.html
11. Boosted Tree Example - Evaluation.mp4
11. Boosted Tree Example - Evaluation.srt
12. Quiz 4.html
2. Boosted Tree working explained.mp4
2. Boosted Tree working explained.srt
3. Types of Boosting.mp4
3. Types of Boosting.srt
4. Options in Create model query - Part 1.mp4
4. Options in Create model query - Part 1.srt
4.1 DART.html
4.2 XgBoost parameters.html
5. Options in Create model query - Part 2.mp4
5. Options in Create model query - Part 2.srt
6. Boosted Tree Example - Use Case Intro & EDA.mp4
6. Boosted Tree Example - Use Case Intro & EDA.srt
7. Boosted Tree Example - Feature Engineering Part 1.mp4
7. Boosted Tree Example - Feature Engineering Part 1.srt
8. Boosted Tree Example - Feature Engineering Part 2.mp4
8. Boosted Tree Example - Feature Engineering Part 2.srt
8.2 Date Functions.html
8.3 Geography Functions in BigQuery.html
9. Boosted Tree Example - Create model.mp4
9. Boosted Tree Example - Create model.srt
9.1 Boosted Tree Hyperparameters.html
Boosted tree
Data definition.txt
EDA.txt
create model.txt
house_sales.csv
11. Model management Operations in BigQuery
1. Introduction.mp4
1. Introduction.srt
2. Operations on Models - Part 1.mp4
2. Operations on Models - Part 1.srt
2.1 bq operations.txt
3. Operations on Models - Part 2.mp4
3. Operations on Models - Part 2.srt
3.1 bq operations.txt
3.2 Export model formats.html
12. Deep Neural Network (DNN)
1. What is Artificial Neural Network.mp4
1. What is Artificial Neural Network.srt
10. Other Options in DNN.html
11. DNN Example Use Case.mp4
11. DNN Example Use Case.srt
11.2 Use case link.html
12. DNN Example Implementation in BigQuery ML.mp4
12. DNN Example Implementation in BigQuery ML.srt
12.1 DNN query.txt
13. Quiz.html
14. Assignment - DNN.html
2. Working of Artificial Neural Network.mp4
2. Working of Artificial Neural Network.srt
3. DNN working explained.mp4
3. DNN working explained.srt
4. Activation Functions - Sigmoid, TanH.mp4
4. Activation Functions - Sigmoid, TanH.srt
5. Activation Functions - RELU.mp4
5. Activation Functions - RELU.srt
6. Which Activation Function to choose.mp4
6. Which Activation Function to choose.srt
7. Dropout technique to avoid Overfitting.mp4
7. Dropout technique to avoid Overfitting.srt
8. HIDDEN_UNITS Option.mp4
8. HIDDEN_UNITS Option.srt
9. Optimizer in DNN.mp4
9. Optimizer in DNN.srt
UCI HAR Dataset
DS_Store
README.txt
activity_labels.txt
features.txt
features_info.txt
test
Inertial Signals
body_acc_x_test.txt
body_acc_y_test.txt
body_acc_z_test.txt
body_gyro_x_test.txt
body_gyro_y_test.txt
body_gyro_z_test.txt
total_acc_x_test.txt
total_acc_y_test.txt
total_acc_z_test.txt
X_test.txt
subject_test.txt
y_test.txt
train
Inertial Signals
body_acc_x_train.txt
body_acc_y_train.txt
body_acc_z_train.txt
body_gyro_x_train.txt
body_gyro_y_train.txt
body_gyro_z_train.txt
total_acc_x_train.txt
total_acc_y_train.txt
total_acc_z_train.txt
X_train.txt
subject_train.txt
y_train.txt
__MACOSX
UCI HAR Dataset
_.DS_Store
_README.txt
_activity_labels.txt
_features.txt
_features_info.txt
_test
_train
test
Inertial Signals
_body_acc_x_test.txt
_body_acc_y_test.txt
_body_acc_z_test.txt
_body_gyro_x_test.txt
_body_gyro_y_test.txt
_body_gyro_z_test.txt
_total_acc_x_test.txt
_total_acc_y_test.txt
_total_acc_z_test.txt
_Inertial Signals
_X_test.txt
_subject_test.txt
_y_test.txt
train
Inertial Signals
_body_acc_x_train.txt
_body_acc_y_train.txt
_body_acc_z_train.txt
_body_gyro_x_train.txt
_body_gyro_y_train.txt
_body_gyro_z_train.txt
_total_acc_x_train.txt
_total_acc_y_train.txt
_total_acc_z_train.txt
_Inertial Signals
_X_train.txt
_subject_train.txt
_y_train.txt
_UCI HAR Dataset
13. BigQuery ML Pricing
1. Free operations in BigQuery ML.mp4
1. Free operations in BigQuery ML.srt
1.1 BigQuery ML pricing.html
2. What is Flat rate pricing model.mp4
2. What is Flat rate pricing model.srt
3. Costs involved in Flat rate pricing model.mp4
3. Costs involved in Flat rate pricing model.srt
3.1 Trial slots form.html
4. Reservations.mp4
4. Reservations.srt
5. BigQuery ML On-demand pricing model.mp4
5. BigQuery ML On-demand pricing model.srt
5.1 BigQuery ML Pricing.html
6. Calculate price for Create Model query.mp4
6. Calculate price for Create Model query.srt
6.1 Pricing.txt
7. Quiz - Pricing.html
14. ARIMA+ for Time series Forecasting
1. What is Time series Forecasting .mp4
1. What is Time series Forecasting .srt
10. ARIMA+ Example - Evaluation.mp4
10. ARIMA+ Example - Evaluation.srt
11. ARIMA+ Example - Inferencing Functions.mp4
12. ARIMA+ Example - Model Explainability.mp4
2. Components of Time series.mp4
2. Components of Time series.srt
3. Stationarity in Time series.mp4
3. Stationarity in Time series.srt
4. Auto regression (AR) in ARIMA.mp4
4. Auto regression (AR) in ARIMA.srt
5. Moving Average (MA) in ARIMA.mp4
5. Moving Average (MA) in ARIMA.srt
6. ARIMA+ Options - Part 1.mp4
6. ARIMA+ Options - Part 1.srt
7. ARIMA+ Options - Part 2.mp4
7. ARIMA+ Options - Part 2.srt
8. ARIMA+ Example - Use case & EDA.mp4
8. ARIMA+ Example - Use case & EDA.srt
9. ARIMA+ Example - Create Model.mp4
9. ARIMA+ Example - Create Model.srt
ARIMA+
Istanbul Weather Data.csv
arima queries.txt
15. Additional Learnings
1. Google Cloud SDK setup.mp4
1. Google Cloud SDK setup.srt
1.1 Google cloud SDK.html
16. BONUS
1. Bonus.mp4
1. Bonus.srt
2. BigQuery ML (BQML) introduction
1. What is BigQuery ML.mp4
1. What is BigQuery ML.srt
2. Conventional ML challenges and How Big query is addressing them.mp4
2. Conventional ML challenges and How Big query is addressing them.srt
3. BigQuery ML Features.mp4
3. BigQuery ML Features.srt
4. Advantages of BigQuery ML.mp4
4. Advantages of BigQuery ML.srt
5. LifecycleWorkflow of a BigQuery ML Project.mp4
5. LifecycleWorkflow of a BigQuery ML Project.srt
6. BQML supported models.mp4
6. BQML supported models.srt
6.1 Supported models in BigQuery ML.html
3. BigQuery Basics - Crash course
1. Announcement.mp4
1. Announcement.srt
2. Setup a GCP account.html
3. Important Note.html
4. Create a Project.mp4
4. Create a Project.srt
5. BigQuery UI Tour.mp4
5. BigQuery UI Tour.srt
6. Create a Dataset.mp4
6. Create a Dataset.srt
7. Create a Table.mp4
7. Create a Table.srt
8. Assignment - Create Table.html
4. Linear Regression
1. What is Linear regression - Part 1.mp4
1. What is Linear regression - Part 1.srt
10. Gradient Descent Optimize Strategy.mp4
10. Gradient Descent Optimize Strategy.srt
11. Types of Gradient Descent.mp4
11. Types of Gradient Descent.srt
12. Learn rate Option.mp4
12. Learn rate Option.srt
13. Other Options in Create model query.mp4
13. Other Options in Create model query.srt
14. Model Training - Write Create model Query for Linear regression.mp4
14. Model Training - Write Create model Query for Linear regression.srt
15. Exploring Model details.mp4
15. Exploring Model details.srt
16. Model Evaluation Query (ML.EVALUATE).mp4
16. Model Evaluation Query (ML.EVALUATE).srt
17. Model Training - Optimize Create Model Query.mp4
17. Model Training - Optimize Create Model Query.srt
18. ML.TRAINING_INFO Function.html
19. Model Prediction (ML.PREDICT).mp4
19. Model Prediction (ML.PREDICT).srt
2. What is Linear regression - Part 2.mp4
2. What is Linear regression - Part 2.srt
20. Quiz 1.html
21. Assignment - Linear regression.html
3. High-level view of Create Model query.mp4
3. High-level view of Create Model query.srt
4. Limitations of Create model query.mp4
4. Limitations of Create model query.srt
5. Linear regression Example Use case.mp4
5. Linear regression Example Use case.srt
6. Basic Options in Create model query.mp4
6. Basic Options in Create model query.srt
7. Overfitting problem.mp4
7. Overfitting problem.srt
8. L2Ridge regularization.mp4
8. L2Ridge regularization.srt
9. L1Lasso regularization.mp4
9. L1Lasso regularization.srt
Linear regression
Linear regression.txt
USA_Housing_train.csv
usa_housing_eval_final.csv
usa_housing_evaluate.csv
usa_housing_predict.csv
5. Hyperparameter Tuning in BigQuery
1. What is Hyperparameter Tuning .mp4
1. What is Hyperparameter Tuning .srt
2. Hyperparameter Tuning Options in BigQuery.mp4
2. Hyperparameter Tuning Options in BigQuery.srt
2.1 Hyperparameter Tuning in Bigquery.html
3. Tune the Linear regression model.mp4
3. Tune the Linear regression model.srt
4. ML.TRIAL_INFO Function.html
6. Model Explainability Functions
1. Why Model Explainability is important .mp4
1. Why Model Explainability is important .srt
2. Model Explainability Functions in BigQuery.mp4
2. Model Explainability Functions in BigQuery.srt
3. ML.WEIGHTS Function.mp4
3. ML.WEIGHTS Function.srt
4. List of functions supported by all models.html
Linear regression
Linear regression.txt
USA_Housing_train.csv
usa_housing_eval_final.csv
usa_housing_evaluate.csv
usa_housing_predict.csv
7. Logistic regression
1. What is Logistic regression .mp4
1. What is Logistic regression .srt
10. Quiz 2.html
11. Assignment - Logistic regression.html
2. Sigmoid Function.mp4
2. Sigmoid Function.srt
3. Logistic regression Example Use case.mp4
3. Logistic regression Example Use case.srt
4. Model Training - Write Create model Query for Logistic regression.mp4
4. Model Training - Write Create model Query for Logistic regression.srt
5. Evaluation metrics Fundamentals explained.mp4
5. Evaluation metrics Fundamentals explained.srt
6. Precision, Recall, Accuracy, F1 score.mp4
6. Precision, Recall, Accuracy, F1 score.srt
7. Evaluation Functions in BigQuery.mp4
7. Evaluation Functions in BigQuery.srt
7.1 Queries.txt
8. Prediction Function (ML.PREDICT).mp4
8. Prediction Function (ML.PREDICT).srt
9. Applications of Logistic regression.mp4
9. Applications of Logistic regression.srt
Logistic regression
Queries.txt
weatherAUS.csv
8. Feature Pre-processing
1. Automatic Feature Pre-processing.mp4
1. Automatic Feature Pre-processing.srt
2. Manual Feature Pre-processing - Part 1.mp4
2. Manual Feature Pre-processing - Part 1.srt
2.1 manual feature preprocessing.txt
3. Manual Feature Pre-processing - Part 2.mp4
3. Manual Feature Pre-processing - Part 2.srt
3.1 manual feature preprocessing.txt
4. FEATURE_INFO Function.html
9. K-means Clustering
1. What is Clustering.mp4
1. What is Clustering.srt
10. Quiz 3.html
11. Assignment - Kmeans Clustering.html
2. K-means algorithm working.mp4
2. K-means algorithm working.srt
3. Advantages & Disadvantages of K-means.mp4
3. Advantages & Disadvantages of K-means.srt
4. Applications of K-means algorithm.mp4
4. Applications of K-means algorithm.srt
5. Options in Create model query.mp4
5. Options in Create model query.srt
6. K-means Example in BigQuery - Create model.mp4
6. K-means Example in BigQuery - Create model.srt
6.1 K-means Queries.txt
7. K-means Example in BigQuery - Evaluation.mp4
7. K-means Example in BigQuery - Evaluation.srt
8. K-means Example in BigQuery - Prediction.mp4
8. K-means Example in BigQuery - Prediction.srt
8.1 K-means Queries.txt
9. K-means Example in BigQuery - Anomaly detection.mp4
9. K-means Example in BigQuery - Anomaly detection.srt
Bonus Resources.txt
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