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Subject Matter Expert
With the paradigm shift of Digital Transformation in industries, there exists a huge volume of digital data in cloud storage about the Men, Materials and Machines of the organization. This data incurs a lot of information which could be used for process planning, predictive failures and business optimization. This course aims to equip the learners with various strategic principles of Artificial Intelligence theory which helps to extract such information from the pool of available data. The reach of AI in every area is consistently growing along with the features of programming. The course introduces appropriate programming skills blended in the modules and the learners will be able to learn by doing lots of practice problems. The long-term vision of AI, with Edge operations are explained in the course along with the principles required in implementing Edge AI. The learner can distinguish and will be able to segment the cloud and edge-based operations appropriately for the real-world problems. The different exercise problems with relevant software and hardware architecture support the learning of Edge AI with suitable metrics. On the whole the learners will get an exciting journey of understanding and applying AI algorithms, processing the algorithms for edge and implementing sample edge AI solutions. Edge AI products available in the market are introduced to the learners and this provides the learners with an ability to map their AI skills with suitable upcoming career options.
Register for this course and see more details by visiting:
OpenCourser.com/course/rtwimw/ai
What's inside
Syllabus
Artificial Intelligence (AI) and its Next Wave - Edge Computing
By the end of this module on Artificial Intelligence and its Next Wave -Edge Computing, learners will be able to :Understand the scope of AI and Edge Computing; Able to acquire the skills in industries using the Edge AI technology; Able to interpret the role of edge computing in IoT
Read more
Syllabus
Artificial Intelligence (AI) and its Next Wave - Edge Computing
By the end of this module on Artificial Intelligence and its Next Wave -Edge Computing, learners will be able to :Understand the scope of AI and Edge Computing; Able to acquire the skills in industries using the Edge AI technology; Able to interpret the role of edge computing in IoT
Read more
Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals
By the end of this module on Python Demos and Case-Studies on Machine Learning (ML) Algorithm Fundamentals, learners will be able to: interpret errors in machine learning, such as bias and variance; Gain insights of implementing ML in real-time domains such as healthcare, banking, and industries; Acquire the capability to perform Exploratory Data Analysis (EDA) processes using the Python programming language; Develop the skills to model ML algorithms for predicting lung cancer disease
Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos
By the end of this module on Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos, learners will be able to: Model a k-means clustering algorithm through a demo; Develop an application demo employing DBSCAN clustering on a dataset; Demonstrate the use of COBOTs in industrial automation.
Principles and Successful Demonstrations of Neural Networks (Text Analytics)
By the end of this module on Principles and successful demonstrations of Neural Networks (Text Analytics), learners will be able to: demonstrate digit recognition using MLP and CNN; Develop a Python program to identify overfitting and underfitting issues in an ML model; develop an ML network using the WEKA tool.
Advanced Applications with Deep Learning Networks
By the end of this module on Advanced Applications with Deep Learning Networks, learners will be able to: Identify the vanishing and unstable gradient problems in a deep learning model; Apply DL for banana leaf disease detection; Apply CNN for Pneumonia Detection; Model an advanced CNN-based ML system to recognize images
IoT with AI and edge computing
By the end of this module on IoT with AI and edge computing, learners will be able to: Understand the working principles of the TinyML system; Identify the need for compression techniques; Interpret High Computing Machine based Edge Architecture; Learn the functionalities of the Arduino IDE and programming on the Arduino Nano BLE development board
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Subject Matter Experts, who are professionals in their industry, which provides credibility to the course material
Develops skills in Edge AI, which is a growing field with a high demand for skilled professionals
Includes Python demos and case studies, which helps learners develop practical skills in Machine Learning
Covers Neural Networks, Deep Learning Networks, and IoT, which are all essential technologies for Edge AI
Provides an understanding of the working principles of TinyML systems and High Computing Machine based Edge Architecture
Includes hands-on labs and interactive materials, which helps learners apply their knowledge and develop practical skills
Save this course
Save AI Principles with Edge Computing to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in AI Principles with Edge Computing with these
activities:
Explore an AI Textbook
Show steps
Supplement your course materials by reading an authoritative textbook on Artificial Intelligence, broadening your understanding of foundational concepts and applications.
View
Artificial Intelligence: A Modern Approach,...
on Amazon
Show steps
-
Obtain a copy of the textbook.
-
Read the chapters relevant to the course topics.
-
Take notes and highlight key concepts.
Review AI
Show steps
Revisit the core concepts and theories of Artificial Intelligence, strengthening your foundation before embarking on this course.
Browse courses on
Artificial Intelligence
Show steps
-
Reread the foundational chapters of your AI textbook or online resources.
-
Take an online refresher course or watch video tutorials on AI.
-
Practice solving simple AI problems and algorithms.
Explore Edge AI Concepts
Show steps
Familiarize yourself with the emerging field of Edge AI and its applications in various industries, broadening your perspective.
Browse courses on
Edge AI
Show steps
-
Follow online tutorials that introduce the fundamentals of Edge AI.
-
Explore case studies and examples of Edge AI implementations.
-
Experiment with Edge AI development platforms or tools.
Three other activities
Expand to see all activities and additional details
Show all six activities
Seek Guidance from Experts
Show steps
Connect with experienced practitioners in the field of AI and Edge Computing, gaining valuable insights and advice.
Show steps
-
Attend industry events or online forums related to AI.
-
Reach out to professionals in your network for introductions.
-
Consider joining online communities or mentorship programs focused on AI.
Practice Python for AI
Show steps
Solidify your Python programming skills, ensuring you have the necessary proficiency to effectively implement AI algorithms.
Browse courses on
Python Programming
Show steps
-
Review fundamental Python concepts and syntax.
-
Solve coding challenges and exercises related to AI and machine learning.
-
Build small Python projects that incorporate AI techniques.
Develop a Mini AI Model
Show steps
Apply your knowledge by creating a simple AI model to solve a specific problem, showcasing your understanding of AI principles.
Show steps
-
Identify a problem or task that can be addressed using AI.
-
Gather and prepare the necessary data for training the model.
-
Choose an appropriate AI algorithm and develop the model.
-
Train and evaluate the model's performance.
-
Document your approach and findings in a report or presentation.
Explore an AI Textbook
Show steps
Supplement your course materials by reading an authoritative textbook on Artificial Intelligence, broadening your understanding of foundational concepts and applications.
View
Artificial Intelligence: A Modern Approach,...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the textbook.
- Read the chapters relevant to the course topics.
- Take notes and highlight key concepts.
Review AI
Show steps
Revisit the core concepts and theories of Artificial Intelligence, strengthening your foundation before embarking on this course.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Reread the foundational chapters of your AI textbook or online resources.
- Take an online refresher course or watch video tutorials on AI.
- Practice solving simple AI problems and algorithms.
Explore Edge AI Concepts
Show steps
Familiarize yourself with the emerging field of Edge AI and its applications in various industries, broadening your perspective.
Browse courses on
Edge AI
Show steps
Show steps
Show steps
- Follow online tutorials that introduce the fundamentals of Edge AI.
- Explore case studies and examples of Edge AI implementations.
- Experiment with Edge AI development platforms or tools.
Three other activities
Expand to see all activities and additional details
Show all six activities
Seek Guidance from Experts
Show steps
Connect with experienced practitioners in the field of AI and Edge Computing, gaining valuable insights and advice.
Show steps
Show steps
Show steps
- Attend industry events or online forums related to AI.
- Reach out to professionals in your network for introductions.
- Consider joining online communities or mentorship programs focused on AI.
Practice Python for AI
Show steps
Solidify your Python programming skills, ensuring you have the necessary proficiency to effectively implement AI algorithms.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review fundamental Python concepts and syntax.
- Solve coding challenges and exercises related to AI and machine learning.
- Build small Python projects that incorporate AI techniques.
Develop a Mini AI Model
Show steps
Apply your knowledge by creating a simple AI model to solve a specific problem, showcasing your understanding of AI principles.
Show steps
Show steps
Show steps
- Identify a problem or task that can be addressed using AI.
- Gather and prepare the necessary data for training the model.
- Choose an appropriate AI algorithm and develop the model.
- Train and evaluate the model's performance.
- Document your approach and findings in a report or presentation.
Career center
Learners who complete AI Principles with Edge Computing will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. The AI Principles with Edge Computing course may be useful for this role because it provides a solid understanding of machine learning theory and algorithms. The course also covers advanced topics such as deep learning and neural networks. Those pursuing a career as a Machine Learning Engineer may benefit from taking this course to build a strong foundation in machine learning and AI.
Data Analyst
Data Analyst
A Data Analyst collects, processes, and analyzes data to identify trends and patterns. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in statistics and data analysis techniques. The course also covers advanced topics such as machine learning, which can be used to automate data analysis tasks. Those interested in a career in Data Analytics may benefit from taking this course to build a strong foundation in data analysis and AI.
Data Scientist
Data Scientist
A Data Scientist combines skills from computer science and statistics to extract, analyze, and interpret data to provide insights for decision-making. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in AI theory and programming skills. The course also covers edge computing, which is becoming increasingly important as more data is generated and processed outside of the cloud. Those interested in a career in Data Science may benefit from taking this course to enhance their skills in AI and edge computing.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and programming skills. The course also covers advanced topics such as edge computing, which is becoming increasingly important in software development. Those interested in a career in Software Engineering may benefit from taking this course to enhance their skills in AI and edge computing.
Business Analyst
Business Analyst
A Business Analyst identifies and solves business problems by analyzing data and recommending solutions. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in business analysis techniques and data interpretation. The course also covers advanced topics such as machine learning and artificial intelligence, which can be used to automate business processes and improve decision-making. Those interested in a career in Business Analysis may benefit from taking this course to build a strong foundation in business analysis and AI.
Product Manager
Product Manager
A Product Manager oversees the development and launch of new products. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in product management techniques and customer research. The course also covers advanced topics such as machine learning and artificial intelligence, which can be used to improve product development and marketing. Those interested in a career in Product Management may benefit from taking this course to build a strong foundation in product management and AI.
Sales Engineer
Sales Engineer
A Sales Engineer supports the sales team by providing technical expertise to customers. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and programming skills. The course also covers advanced topics such as edge computing, which is becoming increasingly important in sales and marketing. Those interested in a career in Sales Engineering may benefit from taking this course to build a strong foundation in computer science and AI.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in project management techniques and stakeholder management. The course also covers advanced topics such as machine learning and artificial intelligence, which can be used to automate project management tasks and improve decision-making. Those interested in a career in Project Management may benefit from taking this course to build a strong foundation in project management and AI.
User Experience Designer
User Experience Designer
A User Experience Designer (UX Designer) designs the user interface and experience of software and hardware products. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in user experience design principles and human-computer interaction. The course also covers advanced topics such as machine learning and artificial intelligence, which can be used to improve user experience.
Technical Writer
Technical Writer
A Technical Writer creates documentation for software and hardware products. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and programming skills. The course also covers advanced topics such as edge computing, which is becoming increasingly important in technical writing. Those interested in a career in Technical Writing may benefit from taking this course to build a strong foundation in computer science and AI.
Computer Systems Analyst
Computer Systems Analyst
A Computer Systems Analyst designs, develops, and maintains computer systems. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and programming skills. The course also covers advanced topics such as edge computing, which is becoming increasingly important in systems analysis and design. Those interested in a career in Computer Systems Analysis may benefit from taking this course to build a strong foundation in computer science and AI.
Database Administrator
Database Administrator
A Database Administrator (DBA) designs, builds, and maintains databases. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in database theory and programming skills. The course also covers advanced topics such as edge computing, which is becoming increasingly important in database design and management. Those interested in a career in Database Administration may benefit from taking this course to build a strong foundation in computer science and AI.
IT Auditor
IT Auditor
An IT Auditor evaluates the security and compliance of computer systems and networks. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and security principles. The course also covers advanced topics such as edge computing, which is becoming increasingly important in security auditing. Those interested in a career in IT Auditing may benefit from taking this course to build a strong foundation in computer science and AI.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects computer systems and networks from cyberattacks. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and security principles. The course also covers advanced topics such as edge computing, which is becoming increasingly important in information security. Those interested in a career in Information Security may benefit from taking this course to build a strong foundation in computer science and AI.
Network Engineer
Network Engineer
A Network Engineer designs, builds, and maintains computer networks. The AI Principles with Edge Computing course may be useful for this role because it provides a foundation in computer science theory and networking skills. The course also covers advanced topics such as edge computing, which is becoming increasingly important in network design and management. Those interested in a career in Network Engineering may benefit from taking this course to build a strong foundation in computer science and AI.
For more career information including salaries, visit:
OpenCourser.com/course/rtwimw/ai
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AI Principles with Edge Computing.
Provides a comprehensive overview of machine learning techniques, with a focus on practical implementation using popular Python libraries.
Provides a practical introduction to deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Focuses on the practical aspects of deep learning using the Fastai library. Covers topics such as computer vision, natural language processing, and time series analysis.
Focuses on the practical aspects of implementing machine learning on tiny microcontrollers, covering topics such as model optimization and hardware constraints.
Provides a comprehensive overview of IoT concepts, including hardware, software, and applications.
Provides a comprehensive guide to machine learning in Python, covering topics such as data preprocessing, feature engineering, model evaluation, and deployment.
Covers the fundamentals of machine learning, including supervised and unsupervised learning, model evaluation, and feature engineering.
Focuses on the practical aspects of AI, with a focus on Python implementation. Covers topics such as natural language processing, computer vision, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rtwimw/ai
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Level
Intermediate
Via
Coursera
Institution
L&T EduTech
Instructor
Subject Matter Expert
Language
English
This course belongs to a
collection
called
Intelligent Digital Factories. It's comprised of four courses:
- Industrial Applications of AI
- Applied Industrial Internet of Things
- AI Principles with Edge Computing (viewing)
- Microcontroller and Industrial Applications
Good to know
Taught by Subject Matter Experts, who are professionals in their industry, which provides credibility to the course material
Develops skills in Edge AI, which is a growing field with a high demand for skilled professionals
Includes Python demos and case studies, which helps learners develop practical skills in Machine Learning
Covers Neural Networks, Deep Learning Networks, and IoT, which are all essential technologies for Edge AI
Provides an understanding of the working principles of TinyML systems and High Computing Machine based Edge Architecture
Includes hands-on labs and interactive materials, which helps learners apply their knowledge and develop practical skills
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