Visit This Web URL https://masterytrail.com/product/accredited-expert-level-sap-hana-predictive-library-advanced-video-course Lesson 1: Overview of SAP HANA Predictive Library
1.1. Introduction to SAP HANA
1.2. What is the Predictive Library?
1.3. Key Features and Benefits
1.4. Use Cases and Applications
1.5. Prerequisites for the Course
1.6. Setting Up the Environment
1.7. Installing SAP HANA Predictive Library
1.8. Navigating the SAP HANA Studio
1.9. Introduction to Predictive Scenarios
1.10. Hands-On: First Predictive Model
Lesson 2: Data Preparation and Exploration
2.1. Importing Data into SAP HANA
2.2. Data Cleaning Techniques
2.3. Data Transformation
2.4. Exploratory Data Analysis (EDA)
2.5. Visualizing Data in SAP HANA
2.6. Handling Missing Values
2.7. Feature Engineering
2.8. Data Normalization and Standardization
2.9. Creating Training and Test Datasets
2.10. Hands-On: Data Preparation Exercise
Lesson 3: Basic Statistical Analysis
3.1. Descriptive Statistics
3.2. Probability Distributions
3.3. Hypothesis Testing
3.4. Correlation and Covariance
3.5. ANOVA and Chi-Square Tests
3.6. Confidence Intervals
3.7. Sampling Techniques
3.8. Statistical Significance
3.9. Interpreting Statistical Results
3.10. Hands-On: Statistical Analysis Exercise
Lesson 4: Introduction to Machine Learning
4.1. Types of Machine Learning
4.2. Supervised Learning
4.3. Unsupervised Learning
4.4. Reinforcement Learning
4.5. Model Evaluation Metrics
4.6. Overfitting and Underfitting
4.7. Bias-Variance Tradeoff
4.8. Cross-Validation Techniques
4.9. Hyperparameter Tuning
4.10. Hands-On: Basic ML Model
Module 2: Supervised Learning Techniques
Lesson 5: Linear Regression
5.1. Introduction to Linear Regression
5.2. Simple Linear Regression
5.3. Multiple Linear Regression
5.4. Assumptions of Linear Regression
5.5. Interpreting Coefficients
5.6. Residual Analysis
5.7. Regularization Techniques
5.8. Ridge and Lasso Regression
5.9. Elastic Net Regression
5.10. Hands-On: Linear Regression Exercise
Lesson 6: Logistic Regression
6.1. Introduction to Logistic Regression
6.2. Binary Logistic Regression
6.3. Multinomial Logistic Regression
6.4. Odds Ratios and Log-Odds
6.5. Confusion Matrix
6.6. ROC Curve and AUC
6.7. Handling Imbalanced Data
6.8. Regularization in Logistic Regression
6.9. Interpreting Logistic Regression Results
6.10. Hands-On: Logistic Regression Exercise
Lesson 7: Decision Trees and Random Forests
7.1. Introduction to Decision Trees
7.2. Building a Decision Tree
7.3. Pruning Techniques
7.4. Introduction to Random Forests
7.5. Building a Random Forest Model
7.6. Feature Importance
7.7. Hyperparameter Tuning for Random Forests
7.8. Bagging and Boosting
7.9. Gradient Boosting Machines
7.10. Hands-On: Decision Trees and Random Forests Exercise
Lesson 8: Support Vector Machines (SVM)
8.1. Introduction to SVM
8.2. Linear SVM
8.3. Non-Linear SVM with Kernels
8.4. Support Vectors and Margins
8.5. SVM for Classification
8.6. SVM for Regression
8.7. Hyperparameter Tuning for SVM
8.8. Advantages and Limitations of SVM
8.9. SVM in SAP HANA
8.10. Hands-On: SVM Exercise
Module 3: Unsupervised Learning Techniques
Lesson 9: Clustering Algorithms
9.1. Introduction to Clustering
9.2. K-Means Clustering
9.3. Hierarchical Clustering
9.4. DBSCAN Clustering
9.5. Choosing the Number of Clusters
9.6. Evaluating Clustering Results
9.7. Clustering in SAP HANA
9.8. Applications of Clustering
9.9. Advanced Clustering Techniques
9.10. Hands-On: Clustering Exercise
Lesson 10: Principal Component Analysis (PCA)
10.1. Introduction to PCA
10.2. Dimensionality Reduction
10.3. Eigenvalues and Eigenvectors
10.4. Variance Explained
10.5. PCA in SAP HANA
10.6. Applications of PCA
10.7. Interpreting PCA Results
10.8. PCA for Feature Selection
10.9. Limitations of PCA
10.10. Hands-On: PCA Exercise
Lesson 11: Association Rule Mining
11.1. Introduction to Association Rule Mining
11.2. Apriori Algorithm
11.3. Eclat Algorithm
11.4. FP-Growth Algorithm
11.5. Support, Confidence, and Lift
11.6. Association Rule Mining in SAP HANA
11.7. Applications of Association Rules
11.8. Evaluating Association Rules
11.9. Advanced Techniques in Association Rule Mining
11.10. Hands-On: Association Rule Mining Exercise
Lesson 12: Anomaly Detection
12.1. Introduction to Anomaly Detection
12.2. Statistical Methods for Anomaly Detection
12.3. Machine Learning Methods for Anomaly Detection
12.4. Isolation Forest
12.5. One-Class SVM
12.6. Anomaly Detection in SAP HANA
12.7. Applications of Anomaly Detection
12.8. Evaluating Anomaly Detection Models
12.9. Advanced Anomaly Detection Techniques
12.10. Hands-On: Anomaly Detection Exercise
Module 4: Advanced Machine Learning Techniques
Lesson 13: Neural Networks and Deep Learning
13.1. Introduction to Neural Networks
13.2. Perceptron Model
13.3. Multi-Layer Perceptrons (MLP)
13.4. Activation Functions
13.5. Backpropagation Algorithm
13.6. Convolutional Neural Networks (CNN)
13.7. Recurrent Neural Networks (RNN)
13.8. Long Short-Term Memory (LSTM)
13.9. Deep Learning in SAP HANA
13.10. Hands-On: Neural Networks Exercise
Lesson 14: Ensemble Learning
14.1. Introduction to Ensemble Learning
14.2. Bagging Techniques
14.3. Boosting Techniques
14.4. Stacking Techniques
14.5. Ensemble Learning in SAP HANA
14.6. Applications of Ensemble Learning
14.7. Evaluating Ensemble Models
14.8. Advanced Ensemble Techniques
14.9. Hyperparameter Tuning for Ensemble Models
14.10. Hands-On: Ensemble Learning Exercise
Lesson 15: Time Series Analysis
15.1. Introduction to Time Series Analysis
15.2. Stationarity and Differencing
15.3. Autocorrelation and Partial Autocorrelation
15.4. ARIMA Models
15.5. Seasonal Decomposition
15.6. Time Series Forecasting in SAP HANA
15.7. Applications of Time Series Analysis
15.8. Evaluating Time Series Models
15.9. Advanced Time Series Techniques
15.10. Hands-On: Time Series Analysis Exercise
Lesson 16: Natural Language Processing (NLP)
16.1. Introduction to NLP
16.2. Text Preprocessing Techniques
16.3. Tokenization and Lemmatization
16.4. Bag of Words (BoW) Model
16.5. TF-IDF Vectorization
16.6. Word Embeddings
16.7. Sentiment Analysis
16.8. NLP in SAP HANA
16.9. Applications of NLP
16.10. Hands-On: NLP Exercise
Module 5: Model Deployment and Optimization
Lesson 17: Model Deployment
17.1. Introduction to Model Deployment
17.2. Deploying Models in SAP HANA
17.3. Integrating Models with SAP Applications
17.4. Model Serving and APIs
17.5. Monitoring Deployed Models
17.6. Scaling Deployed Models
17.7. Security Considerations for Deployed Models
17.8. Version Control for Models
17.9. Continuous Integration and Deployment (CI/CD)
17.10. Hands-On: Model Deployment Exercise
Lesson 18: Model Optimization
18.1. Introduction to Model Optimization
18.2. Hyperparameter Tuning Techniques
18.3. Grid Search and Random Search
18.4. Bayesian Optimization
18.5. Model Pruning and Quantization
18.6. Optimizing Model Performance in SAP HANA
18.7. Profiling and Benchmarking Models
18.8. Advanced Optimization Techniques
18.9. Trade-offs in Model Optimization
18.10. Hands-On: Model Optimization Exercise
Lesson 19: Performance Tuning in SAP HANA
19.1. Introduction to Performance Tuning
19.2. Query Optimization Techniques
19.3. Indexing Strategies
19.4. Partitioning Data
19.5. Memory Management in SAP HANA
19.6. Parallel Processing and Concurrency
19.7. Performance Monitoring Tools
19.8. Advanced Performance Tuning Techniques
19.9. Best Practices for Performance Tuning
19.10. Hands-On: Performance Tuning Exercise
Lesson 20: Scalability and High Availability
20.1. Introduction to Scalability
20.2. Horizontal and Vertical Scaling
20.3. Load Balancing Techniques
20.4. High Availability in SAP HANA
20.5. Failover and Recovery Strategies
20.6. Clustering and Replication
20.7. Scaling Machine Learning Models
20.8. Distributed Computing in SAP HANA
20.9. Advanced Scalability Techniques
20.10. Hands-On: Scalability and High Availability Exercise
Module 6: Advanced Topics in SAP HANA Predictive Library
Lesson 21: Advanced Data Preprocessing
21.1. Advanced Data Cleaning Techniques
21.2. Feature Engineering for Complex Data
21.3. Handling Imbalanced Datasets
21.4. Synthetic Data Generation
21.5. Data Augmentation Techniques
21.6. Advanced Data Transformation
21.7. Feature Selection Techniques
21.8. Dimensionality Reduction Techniques
21.9. Advanced Data Visualization
21.10. Hands-On: Advanced Data Preprocessing Exercise
Lesson 22: Advanced Statistical Analysis
22.1. Advanced Hypothesis Testing
22.2. Multivariate Analysis
22.3. Survival Analysis
22.4. Time-to-Event Analysis
22.5. Advanced Regression Techniques
22.6. Generalized Linear Models (GLM)
22.7. Advanced ANOVA Techniques
22.8. Bayesian Statistics
22.9. Advanced Statistical Modeling
22.10. Hands-On: Advanced Statistical Analysis Exercise
Lesson 23: Advanced Machine Learning Algorithms
23.1. Advanced Supervised Learning Algorithms
23.2. Advanced Unsupervised Learning Algorithms
23.3. Reinforcement Learning in SAP HANA
23.4. Transfer Learning Techniques
23.5. Meta-Learning Techniques
23.6. Advanced Neural Network Architectures
23.7. Generative Adversarial Networks (GANs)
23.8. Advanced Ensemble Learning Techniques
23.9. Advanced Time Series Analysis Techniques
23.10. Hands-On: Advanced Machine Learning Algorithms Exercise
Lesson 24: Advanced NLP Techniques
24.1. Advanced Text Preprocessing Techniques
24.2. Advanced Word Embeddings
24.3. Contextual Embeddings (BERT, ELMo)
24.4. Advanced Sentiment Analysis
24.5. Topic Modeling Techniques
24.6. Named Entity Recognition (NER)
24.7. Text Summarization Techniques
24.8. Machine Translation Techniques
24.9. Advanced NLP Applications
24.10. Hands-On: Advanced NLP Techniques Exercise
Module 7: Real-World Applications and Case Studies
Lesson 25: Predictive Maintenance
25.1. Introduction to Predictive Maintenance
25.2. Data Collection for Predictive Maintenance
25.3. Feature Engineering for Predictive Maintenance
25.4. Building Predictive Maintenance Models
25.5. Evaluating Predictive Maintenance Models
25.6. Deploying Predictive Maintenance Models
25.7. Case Study: Predictive Maintenance in Manufacturing
25.8. Case Study: Predictive Maintenance in Aviation
25.9. Advanced Techniques in Predictive Maintenance
25.10. Hands-On: Predictive Maintenance Exercise
Lesson 26: Customer Churn Prediction
26.1. Introduction to Customer Churn Prediction
26.2. Data Collection for Churn Prediction
26.3. Feature Engineering for Churn Prediction
26.4. Building Churn Prediction Models
26.5. Evaluating Churn Prediction Models
26.6. Deploying Churn Prediction Models
26.7. Case Study: Churn Prediction in Telecom
26.8. Case Study: Churn Prediction in Banking
26.9. Advanced Techniques in Churn Prediction
26.10. Hands-On: Customer Churn Prediction Exercise
Lesson 27: Fraud Detection
27.1. Introduction to Fraud Detection
27.2. Data Collection for Fraud Detection
27.3. Feature Engineering for Fraud Detection
27.4. Building Fraud Detection Models
27.5. Evaluating Fraud Detection Models
27.6. Deploying Fraud Detection Models
27.7. Case Study: Fraud Detection in Finance
27.8. Case Study: Fraud Detection in E-commerce
27.9. Advanced Techniques in Fraud Detection
27.10. Hands-On: Fraud Detection Exercise
Lesson 28: Recommendation Systems
28.1. Introduction to Recommendation Systems
28.2. Data Collection for Recommendation Systems
28.3. Feature Engineering for Recommendation Systems
28.4. Building Recommendation Models
28.5. Evaluating Recommendation Models
28.6. Deploying Recommendation Models
28.7. Case Study: Recommendation Systems in E-commerce
28.8. Case Study: Recommendation Systems in Media
28.9. Advanced Techniques in Recommendation Systems
28.10. Hands-On: Recommendation Systems Exercise
Module 8: Advanced Analytics and Visualization
Lesson 29: Advanced Data Visualization
29.1. Introduction to Advanced Data Visualization
29.2. Interactive Visualizations
29.3. Dashboards and Reports
29.4. Visualizing Time Series Data
29.5. Visualizing Geospatial Data
29.6. Visualizing Network Data
29.7. Advanced Visualization Tools
29.8. Best Practices for Data Visualization
29.9. Advanced Visualization Techniques
29.10. Hands-On: Advanced Data Visualization Exercise
Lesson 30: Advanced Analytics Techniques
30.1. Introduction to Advanced Analytics
30.2. Prescriptive Analytics
30.3. Optimization Techniques
30.4. Simulation and Scenario Analysis
30.5. Advanced Forecasting Techniques
30.6. Advanced Analytics in SAP HANA
30.7. Applications of Advanced Analytics
30.8. Evaluating Advanced Analytics Models
30.9. Advanced Analytics Techniques
30.10. Hands-On: Advanced Analytics Exercise
Lesson 31: Integrating SAP HANA with Other Tools
31.1. Introduction to Integration
31.2. Integrating SAP HANA with SAP Analytics Cloud
31.3. Integrating SAP HANA with SAP BW
31.4. Integrating SAP HANA with SAP Data Intelligence
31.5. Integrating SAP HANA with Third-Party Tools
31.6. Data Exchange and ETL Processes
31.7. API Integration
31.8. Advanced Integration Techniques
31.9. Best Practices for Integration
31.10. Hands-On: Integration Exercise
Lesson 32: Advanced Reporting and Dashboards
32.1. Introduction to Advanced Reporting
32.2. Creating Advanced Reports in SAP HANA
32.3. Designing Interactive Dashboards
32.4. Advanced Reporting Tools
32.5. Best Practices for Reporting and Dashboards
32.6. Advanced Reporting Techniques
32.7. Integrating Reports with SAP Applications
32.8. Automating Report Generation
32.9. Advanced Dashboard Design
32.10. Hands-On: Advanced Reporting and Dashboards Exercise
Module 9: Security and Compliance
Lesson 33: Data Security in SAP HANA
33.1. Introduction to Data Security
33.2. Data Encryption Techniques
33.3. Access Control and Authentication
33.4. Role-Based Access Control (RBAC)
33.5. Audit Logging and Monitoring
33.6. Data Masking and Anonymization
33.7. Securing Sensitive Data
33.8. Advanced Security Techniques
33.9. Best Practices for Data Security
33.10. Hands-On: Data Security Exercise
Lesson 34: Compliance and Governance
34.1. Introduction to Compliance and Governance
34.2. Data Governance Frameworks
34.3. Compliance with Regulations (GDPR, HIPAA)
34.4. Data Lineage and Traceability
34.5. Data Quality Management
34.6. Compliance Reporting
34.7. Advanced Compliance Techniques
34.8. Best Practices for Compliance and Governance
34.9. Integrating Compliance with SAP HANA
34.10. Hands-On: Compliance and Governance Exercise
Lesson 35: Ethical Considerations in Predictive Analytics
35.1. Introduction to Ethical Considerations
35.2. Bias and Fairness in Predictive Models
35.3. Transparency and Explainability
35.4. Privacy and Consent
35.5. Ethical Data Collection Practices
35.6. Ethical Model Deployment
35.7. Advanced Ethical Considerations
35.8. Best Practices for Ethical Predictive Analytics
35.9. Integrating Ethics with SAP HANA
35.10. Hands-On: Ethical Considerations Exercise
Lesson 36: Risk Management in Predictive Analytics
36.1. Introduction to Risk Management
36.2. Identifying Risks in Predictive Analytics
36.3. Mitigating Risks in Model Deployment
36.4. Risk Assessment Techniques
36.5. Risk Monitoring and Reporting
36.6. Advanced Risk Management Techniques
36.7. Best Practices for Risk Management
36.8. Integrating Risk Management with SAP HANA
36.9. Case Studies in Risk Management
36.10. Hands-On: Risk Management Exercise
Module 10: Future Trends and Innovations
Lesson 37: Emerging Trends in Predictive Analytics
37.1. Introduction to Emerging Trends
37.2. AI and Machine Learning Trends
37.3. Big Data and Predictive Analytics
37.4. Edge Computing and Predictive Analytics
37.5. Quantum Computing and Predictive Analytics
37.6. Advanced Visualization Trends
37.7. Integrating Emerging Trends with SAP HANA
37.8. Future Directions in Predictive Analytics
37.9. Case Studies in Emerging Trends
37.10. Hands-On: Emerging Trends Exercise
Lesson 38: Innovations in SAP HANA Predictive Library
38.1. Introduction to Innovations in SAP HANA
38.2. New Features and Enhancements
38.3. Advanced Predictive Algorithms
38.4. Integrating Innovations with SAP Applications
38.5. Future Roadmap for SAP HANA Predictive Library
38.6. Advanced Use Cases and Applications
38.7. Best Practices for Innovations
38.8. Hands-On: Innovations Exercise
38.9. Staying Updated with SAP HANA Innovations
38.10. Community and Support Resources
Lesson 39: Advanced Case Studies and Projects
39.1. Introduction to Advanced Case Studies
39.2. Case Study: Predictive Analytics in Healthcare
39.3. Case Study: Predictive Analytics in Retail
39.4. Case Study: Predictive Analytics in Finance
39.5. Case Study: Predictive Analytics in Manufacturing
39.6. Advanced Projects in Predictive Analytics
39.7. Best Practices for Advanced Projects
39.8. Integrating Advanced Projects with SAP HANA
39.9. Hands-On: Advanced Case Studies and Projects Exercise
39.10. Presenting and Documenting Advanced Projects
Lesson 40: Capstone Project and Certification
40.1. Introduction to Capstone Project
40.2. Defining the Capstone Project Scope
40.3. Data Collection and Preparation
40.4. Building the Predictive Model
40.5. Evaluating the Predictive Model
40.6. Deploying the Predictive Model
40.7. Presenting the Capstone Project
40.8. Documenting the Capstone Project
40.9. Preparing for Certification