Certified Artifical Intelligence AI - Advancia training

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Certified Artifical Intelligence AI

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Réf: AIP

Certified Artifical Intelligence AI

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing. You can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.

You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++

In this course, you will implement AI techniques in order to solve business problems. You will:

Specify a general approach to solve a given business problem that uses applied AI and ML.
Collect and refine a dataset to prepare it for training and testing.
Train and tune a machine learning model.
Finalize a machine learning model and present the results to the appropriate audience.
Build linear regression models.
Build classification models.
Build clustering models.
Build decision trees and random forests.
Build support-vector machines (SVMs).
Build artificial neural networks (ANNs).
Promote data privacy and ethical practices within AI and ML projects

Lesson 1: Solving Business Problems Using AI and ML

Topic A: Identify AI and ML Solutions for Business Problems
Topic C: Formulate a Machine Learning Problem
Topic D: Select Appropriate Tools

Lesson 2: Collecting and Refining the Dataset

Topic A: Collect the Dataset
Topic B: Analyze the Dataset to Gain Insights
Topic C: Use Visualizations to Analyze Data
Topic D: Prepare Data

Lesson 3: Setting Up and Training a Model

Topic A: Set Up a Machine Learning Model
Topic B: Train the Model

Lesson 4: Finalizing a Model

Topic A: Translate Results into Business Actions
Topic B: Incorporate a Model into a Long-Term Business Solution

Lesson 5: Building Linear Regression Models

Topic A: Build a Regression Model Using Linear Algebra
Topic B: Build a Regularized Regression Model Using Linear Algebra
Topic C: Build an Iterative Linear Regression Model

Lesson 6: Building Classification Models

Topic A: Train Binary Classification Models
Topic B: Train Multi-Class Classification Models
Topic C: Evaluate Classification Models
Topic D: Tune Classification Models

Lesson 7: Building Clustering Models

Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models

Lesson 8: Building Advanced Models

Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models

Lesson 9: Building Support-Vector Machines

Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression

Lesson 10: Building Artificial Neural Networks

Topic A: Build Multi-Layer Perceptrons (MLP)
Topic B: Build Convolutional Neural Networks (CNN)

Lesson 11: Promoting Data Privacy and Ethical Practices

Topic A: Protect Data Privacy
Topic B: Promote Ethical Practices
Topic C: Establish Data Privacy and Ethics Policies

22/04/2024

22/04/2024

5 Jours

Catégorie

Cloud

Certification :

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