Advanced Embedded System

Prerequisite: Knowledge in C programming, Embedded development using any micro controllers, Software used: STM32Cube

  • Introduction to 32bit embedded system design
  • Overview of STM32F architecture.
  • Programming STM32F4 with HAL libraries and bare metal.
  • Peripheral interfacing including GPIO, PWM, ADC, Timers, Input Capture, Output Capture, Counters, Interrupt, UART, SPI, I2C.

Advanced Embedded System STM32 Industrial AI Course Deep Learning Raspberry Pi Training Tutorials and Project Support

Advanced Embedded System

Introduction to Advanced Embedded System Artificial Intelligence

Prerequisite: Knowledge in python programming Softwares used: Python, Google colab, tensorflow Keras, scikit learn

  • Introduction to Numpy, Data analysis using numpy.
  • Introduction to pandas and data analysis using pandas.
  • Data visualization-pandas numpy matplotlib seaborn,
  • Python packages Tensorflow, keras, Scikit-learn.
  • Data pre-processing including error, missing data handling.
  • Regular expressions in python.
  • Bias and Variance, concept of hyper parameter tuning.
  • Different data classification and regression methods.
  • Supervised and Unsupervised Learning.
  • Machine learning model creation and training and evaluation.

Introduction to Artificial Intelligence Deep Learning

Prerequisite: Knowledge in python programming, basic machine learning concepts Softwares used: Python, Google colab, tensorflow Keras, scikit learn

  • Understanding neural network concepts.
  • Implementation of basic neural network training and saving the model.
  • Basic introduction to feature extraction, dimensionality and multidimensional space.
  • Industry use cases with deep learning framework.
  • Understanding various concepts in image processing.
  • Implement a basic image recognition problem.
  • Parameters and Hyper parameter tuning.
  • Optimization of machine learning model.
  • Validation and benchmarking ML models.

Artificial Intelligence Deep Learning in Raspberry Pi

  • Develop an environment for embedded development.
  • Understanding various methods to implement Machine learning and Deep Learning model in embedded system
  • Optimization of machine learning model to work with embedded boards
  • Validation and benchmarking ML models.
  • Deploying machine learning models in Raspberry Pi.

Shareable Certificate

International & National Level Certification.

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Advanced Embedded System
Advanced Embedded System
Advanced Embedded System
Advanced Embedded System
Advanced Embedded System