Certified Artificial Intelligence (AI) Practitioner(Exam AIP-210)

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Gain expertise in AI and ML for business solutions with the AIP-210 certification course. Prepare to solve real-world problems and excel in data-driven decision-making.

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Certified Artificial Intelligence (AI) Practitioner(Exam AIP-210)

Overview

Dive into the world of Artificial Intelligence (AI) and Machine Learning (ML) to solve business problems effectively. This course provides a structured approach to developing data-driven solutions through AI and ML methodologies.

Objectives

By the end of this course, leaner will be able to:

  • Develop AI solutions for business problems.
  • Prepare and preprocess data for machine learning.
  • Train, evaluate, and fine-tune machine learning models.
  • Build linear regression, forecasting, classification, and clustering models.
  • Construct models using decision trees, random forests, support-vector machines, and neural networks.
  • Operationalize and maintain machine learning models in production.

Prerequisites

  • Familiarity with foundational data science concepts and the machine learning process.
  • Understanding of statistical concepts and summary statistics.
  • Proficiency in Python programming, including the use of libraries like NumPy and pandas.

Course Outline

Module 1: Solving Business Problems Using AI and ML2024-02-20T18:17:12+05:30
  • Identify solutions for business problems.
  • Formulate machine learning problems.
  • Select appropriate ML approaches.
Module 2: Preparing Data2024-02-20T18:00:16+05:30
  • Data collection and transformation.
  • Feature engineering.
  • Working with unstructured data.
Module 3: Training, Evaluating, and Tuning ML Models2024-02-20T18:01:17+05:30
  • Model training and evaluation.
  • Hyperparameter tuning.
Module 4: Building Regression and Forecasting Models2024-02-20T18:06:25+05:30
  • Linear regression models.
  • Time series forecasting models.
Module 5: Operationalizing and Maintaining ML Models2024-02-20T18:07:41+05:30
  • Model deployment and automation.
  • Integration into ML systems.
  • Security and maintenance of ML pipelines.
2024-04-15T18:16:12+05:30

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