Deep Dive into Machine Learning Using Autonomous Database Course

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Deepdive into Machine Learning Using Autonomous Database Course Overview


The Deepdive into Machine Learning Using Autonomous Database course is an online certification program offered by Oracle University. The course provides an in-depth introduction to Machine Learning and Autonomous Database concepts. It is designed to help learners understand the fundamentals of Machine Learning and Autonomous Database and get hands-on experience with feature engineering, model evaluation, and AI development.The program introduces learners to a range of topics, such as the basics of machine learning, deep neural networks, and automated data processing. Through the program, Oracle provides hands-on experience in Autonomous Database with SQL and Oracle Machine Learning.
The course can be beneficial to those who are just starting out or want to increase their knowledge in the areas of Autonomous Database and Machine Learning. It can also be beneficial to those who are experienced in the fields and want to stay up-to-date on the newest advancements. The certification provided by this course provides a job opportunity or career ladder for users to reach higher levels. The certification will also allow employers to recognize the individual’s working knowledge and certifications in this field.
This program also provides an opportunity to improve the utilization of Autonomous Database and Machine Learning concepts in the workspace. With the help of this certification, users can integrate their learning and progress into the workplace by applying their knowledge in activities like developing data-driven applications and improving recommendations.
Deepdive into Machine Learning Using Autonomous Database
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The 1-on-1 Advantage

Get 1on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

You will learn:

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
  • 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)
  • Describe regression modeling
  • Describe the testing of a regression model
  • List the types of regression algorithms (Generalized Linear Models, Neural Network, Support Vector Machines)
  • Overview of attribute importance
  • List the types of attribute importance algorithms (Minimum Description Length, Principal Comp Analysis, CUR matrix decomposition)
  • Describe anomaly detection
  • Explain the anomaly detection algorithm (One-Class Support Vector Machines)
  • Discuss and recognize applicable use cases
  • 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)
  • Describe association rules
  • Explain transactional data
  • Discuss the Apriori algorithm, a type of association algorithm
  • 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
  • Describe time series
  • Select a time series model
  • Explain time series statistics
  • Discuss Exponential Smoothing, a type of time series algorithm
Live Online Training (Duration : 16 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites

1. Basic understanding of SQL and database design
2. Basic programming skills in Python or other scripting language
3. Familiarity with Graphical User Interfaces (GUIs)
4. Understanding of the various machine learning algorithms, such as linear and logistic regression, decision trees and support vector machines
5. Hands-on experience with cloud services and databases, such as Amazon Web Services, Oracle Cloud and other related services

Target Audience


The target audience for this Deepdive into Machine Learning Using Autonomous Database training is anyone interested in learning how to best take advantage of the features and capabilities of Autonomous Database for machine learning
This includes developers, data scientists, and IT professionals already familiar with machine learning and/or Autonomous Database
This course is also suitable for professionals looking to develop a better understanding of Autonomous Database and how it can be used to support machine learning initiatives
It is beneficial for people from beginner to experienced level to be able to apply Autonomous Database for machine learning projects

Learning Objectives of Deepdive into Machine Learning Using Autonomous Database


This course teaches the fundamentals of machine learning using Autonomous Database. Participants will learn how to explore real-world datasets and build models that accurately analyze and predict outputs. Specifically, learners will use SQL and Oracle Machine Learning to build predictive models, learn methods to analyze and visualize data, gain an understanding of Oracle Cloud services and tools, and investigate an Autonomous Database in the cloud. In the end, participants will have the skills to build production-level machine learning models.

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FAQ's

1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
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Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.

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