Al-Powered IoT Systems Engineer
Course title: Outstanding | Learn Python Programming After C / C++
Target group: Mid Level Employee
Level: Foundations
Outstanding | Learn Python Programming After C / C++
Provider
udemy
Description
Are you familiar with C and C++ programming languages and ready to dive into the world of Python? “Learn Python After C C++” is the ideal course for you to leverage your existing programming knowledge and transition smoothly into one of the most versatile and in-demand programming languages. Python has a simple syntax, dynamic libraries, and immense applications across various industries, from web development to machine learning. This course is designed to bridge the gap between low-level languages like C/C++ and Python’s high-level functionality.
In this course, we cover Python fundamentals in an organized and structured way, focusing on how your background in C and C++ will help you excel in Python. By the end, you’ll have gained an in-depth understanding of Python and how it can enhance your career.
Why Learn Python After C C++?
- Python’s simplicity allows for faster development, saving time and effort.
- High-level applications of Python provide more job opportunities in fields like AI and data science.
- Python integrates easily with C/C++, making it ideal for multi-language projects.
- Python’s huge community ensures that there’s plenty of support and resources available for continued learning.
Benefits of Taking This Course
- Enhance your programming portfolio with Python expertise.
- Gain more job opportunities in industries like data analysis, AI, and software development.
- Make complex programming simpler and more efficient.
Achieve fluency in Python, a highly versatile language used by top tech companies.
Target
- Programmers with experience in C and C++
- Software developers seeking to learn Python
- Technology professionals aiming to diversify their skills in AI, data science, or web development
Sector
- Information Technology
- Software Development
- Data Science and Machine Learning
- Web Development and AI Industry sectors
Area
- Programming and Software Engineering
- Data Analysis and Data Science
- Artificial Intelligence and Machine Learning
- Web and Application Development
Learning outcomes
- Master Python fundamentals with confidence using knowledge from C/C++.
- Transition easily from procedural programming to Python’s object-oriented approach.
- Learn how to utilize Python libraries for data science, web scraping, and more.
- Develop Python programs and scripts that solve real-world problems.
Learning content
- Chapter 1: Python Basics: Variables, Data Types, and Operators
- Chapter 2: Control Structures: Loops, Conditional Statements
- Chapter 3: Functions in Python
- Chapter 4: Object-Oriented Programming in Python
- Chapter 5: Working with Libraries and Modules
- Chapter 6: File Handling and Exception Management
- Chapter 7: Data Analysis with Pandas and NumPy
Approach/method
Online
Duration
7 hours on-demand video
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: IOT Protocols
Target group: Mid Level Employee
Level: Foundations
IOT Protocols
Provider
udemy
Description
In this course, learners will be going to learn about various protocols designed for the implementation of the Internet of Things (IoT) applications. The prerequisite for this course will be learners must have basic knowledge about networking and different networking protocols. In this course, learners will be going to learn about Protocol Standardization for the Internet of Things (IoT). After that learners will going to learn about Machine to Machine (M2M) and Wireless Sensor Network (WSN) Protocols. Then learners will also be going to learn about the Radio Frequency Identification (RFID) Protocol. Then new protocol i.e. Modbus Protocol will be studied in this course. After this Modbus protocol, learners will be going to study about Zigbee Protocol, its Architecture, and other important points related to the Zigbee Protocol. Then learner will be going to study some important IP-based protocols in this course. Those IP-based protocols will be Message Queuing Telemetry Transport (MQTT Secure) Protocol, IPv6 over Low -Power Wireless Personal Area Networks (6LoWPAN) Protocol, etc. After successful completion of this course, learners will get the basic idea about the Protocol Standardization for IoT, M2M and WSN Protocols, RFID Protocol, Modbus Protocol, Zigbee Architecture. IP-based Protocols like MQTT (Secure), 6LoWPAN, etc.
Target
- Networking professional
- IoT developers and engineers
- Electronics and telecommunications students
- IT professionals interested in IoT protocols
- Technicians working on IoT device integration
Sector
- Information Technology (IT)
- Telecommunications
- IoT and embedded systems
- Automation and industrial control
Area
- IoT protocol implementation
- Network communication for IoT
- Wireless sensor networks
- Industrial automation and control systems
Learning outcomes
- Learners will going to learn Protocol Standardization for IoT
- Learners will learn about the M2M and WSN Protocols, RFID Protocol
- Learners will also learn about the Modbus Protocol, Zigbee Architecture
- Learners will also going to learn about various IP based Protocols like MQTT (Secure), 6LoWPAN
Learning content
- Two Pillars of the Web
- M2M and WSN Protocols
- SCADA and RFID Protocols
- Modbus Protocol
- ZigBee Protocol
- IP Based Protocols – MQTT, 6LoWPAN
- Addressing Techniques in IOT
Approach/method
Online
Duration
2 hours on-demand video
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Embedding Sensors and Motors Specialization
Target group: Mid Level Employee
Level: Foundations
Embedding Sensors and Motors Specialization
Provider
coursera
Description
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
Applied Learning Project
You will create hardware and firmware solutions for sensors and motors that take real-time data and process it within an embedded environment. You will measure and record metrology data with oscilloscope traces and use the tools within the embedded system to amplify, filter and optimize the signals.
Target
- Graduate students pursuing a Master of Science in Electrical Engineering at CU Boulder
- Aspiring embedded systems engineers seeking hands-on experience with sensors, motors, and real-time data processing
Sector
- Embedded systems in consumer products
- Embedded systems in industrial/automation applications
Area
- Sensors and actuators integration in embedded environments
- Design and implementation of firmware algorithms for sensor/motor interfaces
- Data acquisition, filtering, and metrology within embedded platforms
Learning outcomes
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from University of Colorado Boulder
- tudy a lab experiment or production process and understand how to specify the proper sensor solution for taking real-time process data
- Implement the sensor into an embedded system in both hardware and software
- Modify existing hardware schematic to add sensors and all support circuitry needed to implement the signal chain in existing microprocessor system
- Create hardware and firmware to process the sensor signal and feed data to a microprocessor for further evaluation
Learning content
- Course 1
- Sensors and Sensor Circuit Design
- Course 2
- Motors and Motor Control Circuits
- Course 3
- Pressure, Force, Motion, and Humidity Sensors
- Course 4
- Sensor Manufacturing and Process Control
Approach/method
Online
Duration
4 months to complete /at 10 hours a week
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Build, Train and Deploy ML Models with Keras on Google Cloud
Target group: Mid Level Employee
Level: Foundations
Build, Train and Deploy ML Models with Keras on Google Cloud
Provider
coursera
Description
This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
Target
- Basic Python knowledge
- ML engineers and data scientists focused on model performance and scalability
- Data engineers and ML engineers responsible for data preparation
- Data engineers, ML engineers working with large-scale data
- Beginners to intermediate ML practitioners and model developers
- ML engineers, MLOps engineers, data scientists transitioning to production
Sector
- Science
- Technology, fintech, healthcare analytics, e-commerce
- Tech, data-intensive industries
- Technology, cloud analytics, big data
- Technology, SaaS, research labs
- Cloud services, enterprise analytics, healthcare tech, fintech
Area
- Machine learning model development and experimentation
- Model evaluation, hyperparameter tuning, scalability considerations
- Data ingestion and preprocessing, tf.data pipeline design
- Large-scale data manipulation, performance optimization
- Model construction (Sequential and Functional API)
- End-to-end ML lifecycle: training, deployment, monitoring, and productionization using Vertex AI
Learning outcomes
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
- Design and build a TensorFlow input data pipeline.
- Use the tf.data library to manipulate data in large datasets.
- Use the Keras Sequential and Functional APIs for simple and advanced model creation.
- Train, deploy, and productionalize ML models at scale with Vertex AI.
Learning content
- Module 1
- Introduction to the Course
- Module 2
- Introduction to the TensorFlow Ecosystem
- Module 3
- Design and Build an Input Data Pipeline
- Module 4
- Building Neural Networks with the TensorFlow and Keras API
- Module 5
- Training at Scale with Vertex Al
- Module 6
- Summary
Approach/method
Online
Duration
1 week at 10 hours a week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Introduction to Cloud Infrastructure Technologies
Target group: Junior (Fresh Employee)
Level: Awareness
Introduction to Cloud Infrastructure Technologies
Provider
edX
Description
New to the cloud and not sure where to begin? This introductory course, taught by cloud experts from The Linux Foundation, will help you grasp the basics of cloud computing and comprehend the terminology, tools and technologies associated with today’s top cloud platforms.
Understanding cloud technologies tops the list of most important skills for any developer, system administrator or network computing professional seeking a lucrative career in technology. However, getting started and researching all things cloud can be complicated and time consuming. This course maps out the entire cloud landscape and explains how various tools and platforms fit together.
Experts from The Linux Foundation can help guide you step-by-step as you begin to navigate the cloud. They host some of the world’s leading open source cloud projects and provide training and networking opportunities to educate a talent pool to support those projects, and is a respected, neutral, non-profit education source to provide training for anyone learning how to build and manage cloud infrastructure.
This course gives you a primer on cloud computing and the use of open source software to maximize development and operations. Topics covered include:
- Next-generation cloud technologies: Learn about cloud and container technologies like Docker, Cloud Foundry, and Kubernetes, as well as the tooling around them.
- Scalable and performant compute, storage and network solutions: Get an overview of software defined storage and software defined networking solutions.
- Solutions employed by companies to meet their business demands: Study up on DevOps and continuous integration practices, as well as the deployment tools available to architects to meet and exceed their business goals.
No previous cloud experience is required for this course. “Introduction to Cloud Infrastructure Technologies” gives you the knowledge and tools to make smart decisions about which cloud services and applications to use depending on your needs.
Target
- Newcomers to cloud computing with no prior cloud experience
- Developers, system administrators, and network professionals seeking foundational cloud knowledge
- IT professionals looking to understand cloud concepts to make informed decisions
Sector
- Technology and IT services
- Software development organizations adopting cloud-native approaches
- Open source and nonprofit IT training sectors
Area
- Foundations of cloud computing
- Open source cloud tools and platforms
- Cloud terminology and landscape overview
- Docker, Cloud Foundry, Kubernetes basics
- Software Defined Storage/Networking fundamentals
- DevOps, CI practices, deployment tooling basics
Learning outcomes
- Basics of cloud computing
- Characteristics of the different cloud technologies
- Working knowledge on how to choose the right technology stack for your needs
Learning content
- Virtualization
- Infrastructure as a Service (IaaS)
- Platform as a Service (PaaS)
- Containers
- Containers: Micro OSes for Containers
- Containers: Container Orchestration
- Unikernels
- Microservices
- Software-Defined Networking and Networking for Containers
- Software-Defined Storage and Storage Management for Containers
- DevOps and CI/CD
- Tools for Cloud Infrastructure: Configuration Management
- Tools for Cloud Infrastructure: Build & Release
- Tools for Cloud Infrastructure: Key-Value Pair Store
- Tools for Cloud Infrastructure: Image Building
- Tools for Cloud Infrastructure: Debugging, Logging, and Monitoring for Containerized Applications
- Service Mesh
- Internet of Things (IoT)
- Serverless Computing
- Distributed Tracing
- How to Be Successful in the Cloud
Approach/method
Online
Duration
14 weeks/3-4 hours per week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Cybersecurity for Everyone
Target group: Junior (Fresh Employee)
Level: Awareness
Cybersecurity for Everyone
Provider
Coursera
Description
Cybersecurity affects everyone, including in the delivery of basic products and services. If you or your organization want to better understand how to address your cybersecurity, this is the course for you and your colleagues to take — from seasoned professionals to your non-technical colleagues.
Your instructor, Dr. Charles Harry, has served on the front lines with the NSA (National Security Agency) and as an expert advising corporate and institutional leaders on managing cybersecurity risk. And he brings a rare and engaging perspective to help you learn cybersecurity from the ground up. Cybersecurity for Everyone lays the groundwork to understand and explore the key issues facing policy makers attempting to manage the problem of cybersecurity, from its technical foundations to the domestic and international policy considerations surrounding governance, privacy, and risk management, to applications for achieving the goals of an enterprise, an institution, or a nation. This course is designed for students with some or no background in information technology, whether a novice or active in the cybersecurity field (engineers and computer scientists will learn the broader context and business aspects of cybersecurity), and will provide the principles to understand the current debates shaping a rapidly evolving security landscape.
Target
- Individuals with some or no background in information technology
- Novices in cybersecurity
- Active cybersecurity professionals seeking broader context
- Non-technical colleagues involved in cybersecurity awareness
Sector
- Public and private sector organizations
- Government agencies
- Corporate and institutional entities
Area
- Cybersecurity awareness and foundational knowledge
- Policy and governance in cybersecurity
- Risk management and privacy
- Technical, legal, and international policy considerations
Learning outcomes
- Political Sciences
- Vulnerability
- Cybersecurity
- General Networking
- Security Awareness
- Cyber Risk
- Telecommunications
- Security Management
- Threat Detection
- Data Security
- Computer Security Awareness Training
- Risk Management
- Governance
- Cyber Security Strategy
- Enterprise Security
- Cyber Security Policies
- Infrastructure Security
- Cyber Governance
- Public Safety and National Security
- Cyber Attacks
Learning content
- Module 1
- Cybersecurity for Everyone: Defining Cyber, Security, and Cybersecurity Policy (Week 1)
- Module 2
- Cybersecurity for Everyone: Evolution of the Internet (Week 2)
- Module 3
- Cybersecurity for Everyone: Global Telecommunications Architecture and Governance (Week 3)
- Module 4
- Cybersecurity for Everyone: Threat Actors and Their Motivations (Week 4)
- Module 5
- Cybersecurity for Everyone: The Hacking Process (Week 5)
- Module 6
- Cybersecurity for Everyone: End Effects – Direct and Indirect Consequences (Week 6)
Approach/method
Online
Duration
Approximately 21 hours
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Machine Learning Specialization
Target group: Junior (Fresh Employee)
Level: Awareness
Machine Learning Specialization
Provider
Coursera
Description
- The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
- It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
- By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Applied Learning Project
- By the end of this Specialization, you will be ready to:
- Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
- Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
- Build and train a neural network with TensorFlow to perform multi-class classification.
- Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
- Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
- Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
- Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
- • Build a deep reinforcement learning model.
Target
- Beginners aiming to enter AI/ML
- Professionals seeking foundational ML skills to apply to real-world problems
- Students considering a career in machine learning or data science
Sector
- Technology and software development
- Data science and analytics
- AI research and product development
Area
- Fundamentals of machine learning
- Supervised learning (linear regression, logistic regression, neural networks, decision trees)
- Unsupervised learning (clustering, dimensionality reduction, recommender systems)
- Model evaluation, tuning, and data-centric ML practices
- Practical ML deployment and best practices using Python, NumPy, scikit-learn, and TensorFlow
- Advanced topics: decision trees ensembles, unsupervised methods, recommender systems, deep reinforcement learning
Learning outcomes
- Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
- Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
- Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
- Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Stanford University
Learning content
Approach/method
Online
Duration
2 months to complete/at 10 hours a week
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Real-time Stream Processing with PySpark
Target group: Mid Level Employee
Level: Foundations
Real-time Stream Processing with PySpark
Provider
Pluralsight
Description
Apache Spark is the most widely used analytics engine for large-scale data processing. This course will teach you how to process real-time data streams and productionize real-time data applications.
Handling real-time data streams is crucial for modern applications, but many find it challenging to process and analyze data efficiently as it arrives.
In this course, Real-time Stream Processing with PySpark, you’ll gain the ability to build and deploy scalable, real-time data applications using Apache Spark and Python.
First, you’ll explore the fundamentals of the modern Spark Streaming and structured streaming concepts.
Next, you’ll discover advanced streaming techniques, such as window operations, stateful transformations, and fault tolerance, to enhance the reliability and performance of your applications.
Finally, you’ll learn how to integrate PySpark with various data sources and sinks, enabling seamless data ingestion and output to and from your streaming applications. When you’re finished with this course, you’ll have the skills and knowledge of stream processing with PySpark needed to develop robust, real-time data processing systems that can handle large-scale data streams efficiently.
Target
- Data engineers and developers who want to build and productionize real-time data applications using Apache Spark and Python
Sector
- Technology, finance, e-commerce
- telecommunications, media, and any industry requiring real-time analytics
Area
- Real-time data streaming with Spark (Spark Streaming and Structured Streaming), window operations, stateful transformations, fault tolerance, and data source/sink integration
Learning outcomes
- Understand core concepts of modern Spark Streaming and Structured Streaming, including micro-batch vs. continuous processing models and the role of executors, batches, and triggers.
- Implement real-time data pipelines using PySpark to ingest, process, and output streaming data with correctness and efficiency.
- Apply windowing techniques (tumbling, sliding, and session windows) to aggregate and analyze data over specified time intervals.
- Perform stateful transformations to maintain and query state across micro-batches, enabling complex event processing and pattern detection.
- Ensure fault tolerance and reliability through checkpointing, exactly-once semantics, and robust error handling in streaming queries.
- Integrate PySpark streams with diverse data sources and sinks (e.g., Kafka, Kinesis, HDFS/S3, parquet/JSON/CSV sinks) for end-to-end streaming applications.
- Optimize performance of streaming applications via resource configuration, backpressure handling, trigger strategies, and efficient serialization formats.
- Deploy and productionize real-time streaming applications by packaging PySpark jobs, monitoring workloads, and implementing operational best practices.
Learning content
- Module 1: Introduction to Real-time Stream Processing with PySpark
- Module 2: Fundamentals of Structured Streaming
- Module 3: Windowing and Time-based Aggregations
- Module 4: Stateful Transformations and Fault Tolerance
- Module 5: Data Sources and Sinks
- Module 6: Performance Optimization and Operational Practices
- Module 7: Real-world Projects and Capstone
Approach/method
Online
Duration
1h 4m
Assessment
No
Certification
No
Date
Always available
Location
Online
Website