Implement Machine Learning Using Oracle Data Miner
Duration : 3 Days (24 Hours)
Implement Machine Learning Using Oracle Data Miner Course Overview:
The Oracle Data Miner certification empowers individuals with machine learning capabilities to extract insights and predict future trends from large datasets. This tool, integrated into Oracle’s Advanced Analytics, simplifies data mining processes, enabling users to generate powerful predictions and valuable insights. Certified professionals can create, execute, evaluate, and implement data mining models. Industries leverage Oracle Data Miner for predictive analytics, customer segmentation, identifying factors impacting customer churn, and detecting anomalies. This intelligent data analysis facilitates optimized decision-making, streamlined operations, enhanced customer experiences, and competitive advantages. Ultimately, the certification equips professionals to navigate complex data landscapes and derive meaningful outcomes.
Intended Audience:
- Database developers
- Data analysts
- Data scientists
- Oracle Developers
- Business intelligence professionals
- IT professionals aiming to enhance their data mining skills
- Professionals enthusiastic about learning machine learning algorithms.
Learning Objectives of Implement Machine Learning Using Oracle Data Miner:
1. Understand the concepts, features and architecture of Oracle Access Management 12c.
2. Set up a Weblogic domain and create OAM domain.
3. Understand the capabilities and security measures of Oracle Access Manager.
4. Configure OAM environment, Users and Roles, Authorization, Resources and Agents.
5. Configure Authentication, Authorization, Federation and Web Services Security.
6. Monitor, Troubleshoot and tune OAM environments.
7. Utilize OAM security components such as OAuth2 with OAM access tokens.
8. Configure OAM to integrate with SSO solutions such as LDAP, Kerberos, SAML and OID.
9. Secure web apps, SaaS apps and mobile apps with Oracle Access Management.
10. Implement security protocols within OAM using various features in OAM 12c.
1: Course Overview
2: Fundamentals of Oracle Machine Learning
- Fundamentals of Oracle Machine Learning Part 1
- Fundamentals of Oracle Machine Learning Part 2
3: Introduction to Oracle Machine Learning UIs - Introduction to Oracle Machine Learning UIs Part 1
- Introduction to Oracle Machine Learning UIs Part 2
- Practice 3-1: Create a SQL Developer Connection for the Data Miner User
- Practice 3-2: Install the Data Miner Repository
- Practice 3-3: Create a Data Miner Workflow
4: Using Classification Models - Using Classification Models Part 1
- Using Classification Models Part 2
- Practice 4-1: Select and Examine Titanic Data Source
- Practice 4-2: Perform Transformations to Prepare the Data
- Practice 4-3: Use Attribute Importance to Filter Input Variables
- Practice 4-4: Create Classification Models
- Practice 4-5: Create Classification Models Using Oracle Data Miner Automated OML
5: Using Regression Models - Using Regression Models Part 1
- Using Regression Models Part 2
- Practice 5-1: Select and Examine Boston Housing Data Source
- Practice 5-2: Perform Transformations to Prepare the Data
- Practice 5-3: Use Attribute Importance to Filter Input Variables
- Practice 5-4: Create Regression Models
- Practice 5-5: Create Regression Models Using Oracle Data Miner Automated OML
- 6: Using Clustering Models
- Using Clustering Models Part 1
- Using Clustering Models Part 2
- Practice 6-1: Select and Examine Life Insurance Customers? Data Source
- Practice 6-2: Create Clustering Models
- Practice 6-3: Select and Examine the IRIS Flower Dataset
- Practice 6-4: Create Clustering Models
- Practice 6-5: Create K-Means Clustering Model Without the Species Attribute
- Practice 6-6: Compare the KMeans Models with and Without the SPECIES Column
7: Using Anomaly Detection Models - Using Anomaly Detection Models Part 1
- Using Anomaly Detection Models Part 2
- Practice 7-1: Select and Examine the Auto Insurance Claims Dataset
- Practice 7-2: Create Anomaly Detection Model.
- Practice 7-3: Create Anomaly Detection Model for the Tax Dataset
Implement Machine Learning Using Oracle Data Miner Course Prerequisites:
• Advanced proficiency in SQL
• Strong understanding of data mining concepts
• Familiarity with Oracle Database
• Basic knowledge of statistical concepts
• Experience with Oracle Data Miner GUI
• Understanding of business use-cases for data-driven solutions.
• Prior exposure to machine learning algorithms
Discover the perfect fit for your learning journey
Choose Learning Modality
Live Online
- Convenience
- Cost-effective
- Self-paced learning
- Scalability
Classroom
- Interaction and collaboration
- Networking opportunities
- Real-time feedback
- Personal attention
Onsite
- Familiar environment
- Confidentiality
- Team building
- Immediate application
Training Exclusives
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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