Deepdive into Machine Learning Using Autonomous Database
Duration : 2 Days (16 Hours)
Deepdive into Machine Learning Using Autonomous Database Course Overview:
The Deepdive into Machine Learning Using Autonomous Database certification is a highly esteemed credential that validates a professional’s expertise in applying machine learning techniques using autonomous databases. This certification encompasses a broad range of topics, including data management, predictive analytics, decision trees, and neural networks.
Recognized across industries dealing with extensive data, this certification equips professionals with intelligent solutions to effectively handle and leverage large datasets. With this certification, individuals can develop data models, identify complex patterns and anomalies, and make predictions about future trends.
- Experienced database professionals seeking advanced skills
- IT professionals interested in Machine Learning
- Data analysts hoping to enhance workflows with autonomous databases
- Software engineers exploring ML applications in database management
- Students and researchers in data science or artificial intelligence fields
- Business intelligence professionals looking for automation strategies
Learning Objectives of Deepdive into Machine Learning Using Autonomous Database:
The main objective of the “Deepdive into Machine Learning Using Autonomous Database” course is to provide students with comprehensive knowledge of how to utilize autonomous database for machine learning. Students will learn about the functionalities and advantages of autonomous databases and will gain insight into how machine learning can be applied to these databases for advanced data management and analysis. Other objectives include understanding the principles of machine learning, including its methods, algorithms, and real-world applications. Additionally, the course aims to equip students with practical skills for implementing machine learning in an autonomous database environment.
Module 1: Using Statistical Functions
- An overview of statistical functions
- List the advantages of performing statistical functions inside the database
- Explain the descriptive statistics supported inside the database
- Describe hypothesis testing and work through some examples
- Describe correlation analysis and work through some examples
- Describe cross-tabulations and work through some examples
Module 2: Classification Model
- Overview of classification modeling
- Describe the testing of a classification model
- Describe biasing a classification model
- List the types of classification algorithms (Decision Tree, Naive Bayes, Generalized Linear Models, Random Forest, Support Vector Machines, Neural Network, MSET-SPRT, XGBoost)
Module 3: Regression
- Describe regression modeling
- Describe the testing of a regression model
- List the types of regression algorithms (Generalized Linear Models, Neural Network, Support Vector Machines)
Module 4: Using Attribute Importance
- Overview of attribute importance
- List the types of attribute importance algorithms (Minimum Description Length, Principal Comp Analysis, CUR matrix decomposition)
Module 5: Implementing Anomaly Detection
- Describe anomaly detection
- Explain the anomaly detection algorithm (One-Class Support Vector Machines)
- Discuss and recognize applicable use cases
Module 6: Using Clustering
- Describe clustering
- Explain hierarchical clustering
- Discuss how to evaluate a clustering model
- List the types of clustering algorithms (Expectation Maximization, k-Means, Orthogonal Partitioning Clustering)
Module 7: Association Rules
- Describe association rules
- Explain transactional data
- Discuss the Apriori algorithm, a type of association algorithm
Module 8: Using Feature Selection and Extraction
- Describe feature selection
- Describe feature extraction
- List the types of feature extraction algorithms: Explicit Semantic Analysis Non-Negative Matrix Factorization Singular Value Decomposition Prediction Component Analysis
Module 9: Using Time Series
- Describe time series
- Select a time series model
- Explain time series statistics
- Discuss Exponential Smoothing, a type of time series algorithm
Deepdive into Machine Learning Using Autonomous Database Course Prerequisites:
- Basic understanding of Machine Learning concepts
- Knowledge of SQL and PL/SQL programming
- Familiarity with Oracle Autonomous Database
- Proficiency in Python programming language
- Understanding of cloud computing fundamentals
- Prior hands-on experience with database management and design.
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This course comes with following benefits:
- Practice Labs.
- Get Trained by Certified Trainers.
- Access to the recordings of your class sessions for 90 days.
- Digital courseware
- Experience 24*7 learner support.
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