Easy Learning with Master in Artificial Intelligence (AI)
Development > Data Science
8 h
£29.99 Free for 3 days
4.3
22417 students

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Language: English

Sale Ends: 12 Aug

Become a Master AI Engineer: Machine Learning & Deep Learning

What you will learn:

  • Fundamentals of Artificial Intelligence
  • AI Engineer career paths and responsibilities
  • Effective AI problem-solving strategies
  • Building a successful AI Engineering career
  • AI problem definition methodologies
  • Data acquisition and preprocessing techniques
  • Algorithm selection and model development
  • Advanced feature engineering strategies
  • Model deployment and real-world implementation
  • Model monitoring, maintenance, and optimization
  • Staying current with AI research and innovation

Description

Aspiring to a lucrative career as an AI Engineer? Unlock your potential with this comprehensive course.

You'll learn not just the theory of Artificial Intelligence, but the practical skills to thrive in this rapidly growing field. This program provides a step-by-step guide to building, deploying, and maintaining machine learning and deep learning models designed to tackle complex business challenges.

Here's what you will gain:

  • A deep understanding of AI principles and the AI Engineer role.
  • Hands-on experience developing and deploying machine learning and deep learning models.
  • Proven techniques to solve real-world business problems using AI.
  • Access to numerous free preview lectures to assess the course content.
  • Ongoing support to clarify any questions throughout your learning journey.
  • Udemy's 30-day money-back guarantee, ensuring a risk-free learning experience.

My journey into AI began in 2020, witnessing firsthand the surge in global demand for AI Engineers. I've directly addressed the industry's skill requirements, training many students in this high-demand field. This course distills my extensive experience, equipping you with the knowledge and skills necessary for success.

See what past students are saying:

"Excellent in-depth instruction on defining problems, data collection and preprocessing, and selecting the ideal algorithm."

"This course's structure and content are truly engaging!"

"The AI knowledge is fantastic – a perfect fit for my needs!"

"Amazing!"

"A remarkable course!"

"Truly exceptional work!"

"Incredibly interesting!"

"Very informative!"

"Great!"

Explore numerous free lectures to experience the quality firsthand. If you're satisfied, enroll, and transform your career into a successful AI Engineering career! If not, let's collaborate to refine the course to perfectly meet your expectations.

Don't forget, this course comes with Udemy's 30-day money-back guarantee.

Curriculum

Introduction

This introductory section lays the groundwork for your AI journey, providing a comprehensive overview of the field and setting the stage for your future learning. The "Introduction" lecture provides a detailed overview of the course structure and learning objectives.

Overview

The Overview section offers an in-depth introduction to key AI concepts. Lectures cover various aspects, offering a solid foundation for upcoming modules. Each lecture includes a brief Q&A to reinforce learning.

Problem Definition

Master the art of identifying and defining AI problems. This section guides you through the process of framing problems effectively, followed by a Q&A session for each lecture to delve into specific challenges and solutions.

Data Collection & Preprocessing

Learn to collect and prepare your data for AI model training. Each lecture is followed by a Q&A to address data-related issues. This section covers various preprocessing techniques, ensuring your data is ready for effective model training.

Algorithm Selection & Development

This extensive section dives into choosing and building the right algorithms for your AI solutions. Numerous lectures cover a wide range of algorithms, each followed by a Q&A session to provide detailed clarification on complex concepts and practical implementation.

Feature Engineering

Enhance your models' performance through feature engineering. This section guides you through effective strategies for feature selection and transformation, followed by Q&A sessions for each lecture, strengthening your understanding of this crucial skill.

Deployment

Learn to deploy your trained models into real-world environments. This section covers the practical aspects of deployment, with each lecture offering in-depth instruction and a Q&A session to troubleshoot any deployment challenges.

Monitoring and Maintenance

Ensure your AI models continue to perform optimally. This section explores best practices for monitoring and maintaining models in real-world use cases. Every lecture includes a Q&A session for detailed insights into model maintenance.

Collaboration

AI is a collaborative field. This section focuses on teamwork and communication in AI projects. It includes Q&A sessions to help you understand the collaborative aspects of successful AI work.

Research and Innovation

Stay at the forefront of the AI field. This section introduces you to the latest research and innovations. Each lecture includes a Q&A session that explores current research trends.

Ethical Considerations

Explore the ethical implications of AI. This section discusses responsible AI development, with each lecture supplemented by a Q&A session addressing ethical concerns in AI projects.

Roadmap to become AI Engineer

This section provides a clear path to becoming a successful AI Engineer, outlining the steps and skills needed.

AI Agents

This section provides an overview of AI agents and their role in modern AI systems.

Summary

A concise summary of the key concepts covered throughout the course.

Related Resources

This section offers links to additional resources for continued learning.

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