AI Fundamentals for Non-Data Scientists faq

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This course provides an introduction to AI fundamentals for non-data scientists. Through the use of tools such as Teachable Machine and TensorFlow, participants will gain an understanding of how Machine Learning is used to process and interpret Big Data. Participants will also learn the various methods for creating algorithms to incorporate into their business.

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Go to class

Course Feature

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Free

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Coursera

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languageLanguage:

English

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course, AI Fundamentals for Non-Data Scientists, provides an introduction to Machine Learning (ML) methods, Deep Learning, and their limitations. It also covers how to drive accuracy and use the best training data for algorithms. Additionally, the course explores Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), and how to use AutoML to begin building algorithms.

The course also includes exclusive interviews with industry leaders who manage Big Data for companies like McDonald's and Visa. By the end of the course, participants will have learned how to code in various ways, such as using no-code tools, understanding Deep Learning, measuring and reviewing errors in algorithms, and using Big Data to not only maintain customer privacy but also to develop different strategies that will drive their business.

[Applications]
At the end of this course, participants will be able to apply their newfound knowledge of AI fundamentals to their own projects. They will be able to use no-code tools to code, understand Deep Learning, measure and review errors in their algorithms, and use Big Data to maintain customer privacy and develop different strategies that will drive their business. Additionally, they will be able to use GANs and VAEs to create algorithms that work for them, as well as engage with AutoML to further develop their algorithms.

[Career Paths]
1. AI Engineer: AI Engineers are responsible for developing and deploying AI-based solutions. They use a variety of tools and techniques to build and maintain AI systems, such as machine learning, deep learning, natural language processing, and computer vision. AI Engineers must have a strong understanding of data science and software engineering principles, as well as the ability to work with large datasets. The demand for AI Engineers is growing rapidly, and the field is expected to continue to expand in the coming years.

2. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover patterns and insights. They use a variety of techniques, such as machine learning, natural language processing, and statistical analysis, to uncover meaningful insights from data. Data Scientists must have a strong understanding of mathematics, statistics, and computer science, as well as the ability to work with large datasets. The demand for Data Scientists is growing rapidly, and the field is expected to continue to expand in the coming years.

3. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques to build and maintain machine learning systems, such as deep learning, natural language processing, and computer vision. Machine Learning Engineers must have a strong understanding of data science and software engineering principles, as well as the ability to work with large datasets. The demand for Machine Learning Engineers is growing rapidly, and the field is expected to continue to expand in the coming years.

4. Big Data Engineer: Big Data Engineers are responsible for managing and analyzing large datasets. They use a variety of tools and techniques to store, process, and analyze large datasets, such as Hadoop, Spark, and NoSQL databases. Big Data Engineers must have a strong understanding of data science and software engineering principles, as well as the ability to work with large datasets. The demand for Big Data Engineers is growing rapidly, and the field is expected to continue to expand in the coming years.

[Education Paths]
1. Bachelor's Degree in Computer Science: A Bachelor's Degree in Computer Science is a great way to gain a comprehensive understanding of the fundamentals of AI and its applications. This degree will provide students with the knowledge and skills necessary to develop and implement AI-based solutions. Additionally, students will learn about the latest trends in AI, such as deep learning, natural language processing, and machine learning.

2. Master's Degree in Artificial Intelligence: A Master's Degree in Artificial Intelligence is a great way to gain a deeper understanding of the field. This degree will provide students with the knowledge and skills necessary to develop and implement AI-based solutions. Additionally, students will learn about the latest trends in AI, such as deep learning, natural language processing, and machine learning.

3. Doctorate Degree in Artificial Intelligence: A Doctorate Degree in Artificial Intelligence is the highest level of education available in the field. This degree will provide students with the knowledge and skills necessary to develop and implement AI-based solutions. Additionally, students will learn about the latest trends in AI, such as deep learning, natural language processing, and machine learning.

4. Certificate in Artificial Intelligence: A Certificate in Artificial Intelligence is a great way to gain a basic understanding of the fundamentals of AI and its applications. This certificate will provide students with the knowledge and skills necessary to develop and implement AI-based solutions. Additionally, students will learn about the latest trends in AI, such as deep learning, natural language processing, and machine learning.

Course Syllabus

Big Data Overview

Big Data Analysis

Data Management Tools

Data Management Infrastructure

Data Analysis: Extracting Intelligence from Big Data

Introduction to Artificial Intelligence

Machine Learning Overview

Reinforcement Learning

A Detailed View of Machine Learning

Course Provider

Provider Coursera's Stats at AZClass

The course teaches learners about various machine learning methods, deep learning and their limitations. Learners will understand how to use the best training data for their algorithms and how to improve accuracy. They will also explore generative adversarial networks and variational autoencoders and start building algorithms using AutoML. Through exclusive interviews with industry leaders, learners will gain insight into how companies such as McDonald's and Visa manage big data. Learners will have learned how to code in a variety of ways, such as using no-code tools, understanding deep learning, measuring and reviewing errors in algorithms, and using big data to not only maintain customer privacy, but also develop different strategies to drive their business.

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