Industrial Training Program on Machine Learning and Embedded Systems and IoT

Embedded Systems and IoT

Eduvance conducts a 30-day training program on Machine Learning and Embedded Systems using PIC18. Focusing on key skills required by the industry, the program shall be conducted in 2 phases:

Phase 1: Machine Learning using Python: This training is in collaboration with IBM Edtech Partner and Powered by IBM Cloud. The focus of this training is to give the student hands-on experience to one of the hottest technologies that is revolutionizing the computer industry. The student will be taught on how to implement machine learning algorithms using the IBM Watson platform. After the training the student will also be given a project by IBM Edtech Partner for the student to gain a real-life industry exposure.

Phase 2: Embedded Systems Design using PIC18: One can differentiate your embedded system’s design with Microchip’s 8-bit microcontrollers which provide the performance and functional capabilities to meet design needs across a wide variety of applications. It enables one to easily scale your design to meet market requirements. Students can develop projects with comprehensive and easy-to-use software solutions, including MPLAB® Harmony, Atmel START and a rich integrated development environment which shortens your time to market. Exploring these hardware and software ecosystems helps reduce your overall investment and make embedded designs an effortless experience.This training will focus on the basics of PIC18 microcontrollers, how to use PIC development board, MPLAB® IDE and XC8 Compiler to create projects.

Course content

SECTION 1 : MACHINE LEARNING
    Module 1
  • Basics of Python Scripting
    • Basics of Python
      • Interpreter Vs compiler
      • Installation of Python
      • Basic python syntax
      • Data types conversion
      • Native data types of Python - lists, dictionaries, tuples
  • Conditional Statement in Python
    • Conditional execution
      • If condition
      • for loops
      • while loops
    Module 2
  • Modular programming in Python
    • Functions in python
      • Creating user defined functions
      • Calling functions
  • Advanced scripting techniques in python
    • List comprehensions in Python
      • Using functions in python(variable inputs also)
      • Use of one line for loops
      • Lambda, reduce, map functions
    • Exception Handling
      • Try-Except
      • Custom Exception method
    • File I/O(optional)
    Module 3
  • Understanding Multidimensional Arrays
    • Introduction and installation of numpy
    • Array creation
    • Array indexing
    • Array slicing
    • Boolean indexing (optional)
    • Mathematical operations on matrices
  • Data visualization in Python
    • Installing matplotlib
    • Matplot object api - axes, figure objects
    • Subplot nrow and ncols
    • Legend labels and titles
    • Setting colors, Linewidths and Linetypes
    • Axis range and Axis grid
    • Visualization matplotlib - 2d scatter plot, Bar, Histogram
    Module 5
  • Introduction to Machine Learning
    • Components of Artificial Intelligence
      • Machine Learning
      • Deep Learning
    • Classification of Machine Learning
      • Association Learning
      • Supervised Learning
      • Unsupervised Learning
      • Reinforcement Learning
  • Association Learning
    • Market basket analysis
    • Apriori algorithm
    • Creating Grocery Cart Application
    Module 6
  • Supervised Learning - Regression
    • Linear Regression
      • OLS method
      • Lab based on OLS Method
      • SGD method
      • Lab based on SGD Method
    • Polynomial Regression
      • Lab based on Polynomial Regression
    • Multivariate Regression
      • Lab based on Multivariate Regression
  • Supervised Learning – Classification
    • Perceptron
      • Lab based on Perceptron
    • Support Vector Machine
      • Lab based on Support Vector Machine
    • Decision Tree
      • Lab based on Decision Tree
    Module 7
  • Unsupervised Learning – Clustering
    • K-means Clustering
    • Elbow Method
    • Silhouette score
    • Lab based on K-means
    Module 8
  • IBM Cloud Fundamentals
    • Setting up the IBM Cloud
    • IBM Cloud Dashboard
    • IBM cloud services
  • IBM Watson Fundamentals
    • Understanding IBM Watson machine Learning architecture
    • Provisioning different services useful for ML application
    • Machine Learning Implementation on IBM Watson and Cloud Platform
      • Lab: Working with IBM Datasets
      • Lab: Building regression-based models with IBM Watson services
    • Implementing case study on IBM Cloud Platform
      • Lab: Building Web interface for Machine Learning Model
      • Lab: Interfacing with IBM machine learning API
      • Lab: Calculating performance and accuracy
SECTION 2 : PIC18 MICROCONTROLLERS
    Module 1
  • Microchip Tools
    • Introduction to PIC18 microcontrollers
    • Introduction to PIC18 development board
    • Introduction to MPLAB IDE
    • Introduction to programmers
    • Pre-Lab, Compiler and Plugins Installation
    Module 2
  • Digital I/O concepts
    • TRIS, PORT, LATCH buffers
    • Digital Output
      • Lab: Blinking LED
      • Lab: Blinking LED sequence
    • Digital Input
      • Lab: Switch LED control
      • Lab: Switch LED toggling
  • PWM concepts
    • Understanding PWM signal and generation
    • Data buffers used for PWM
    • Lab: LED Intensity control
    • Lab: Switch based LED intensity control
    Module 3
  • Data Communication concepts
    • Types of communication
    • Serial – UART Communication
    • Data buffers used for UART
    • Lab: Basic data transmission
    • Lab: Basic data transmission and reception
    • Lab: Serial input LED control
    Module 4
  • Concepts of Analog Interfacing
    • Data buffers used for Analog Interfacing
    • Lab: Analog sensor value display
    • Lab: Data Acquisition System
    • Lab: Potentiometer LED PWM control
  • Concepts of Timer
    • Different modes of Timer operations
    • Data buffers used for Timer and Counter applications
    • Lab: Generating delay using different timer modes
    Module 5
  • Internet of Things
    • Internet of Things architecture
    • Applications
    • IoT Cloud – Thingspeak
    • Data analysis and visualization on Thingspeak
    • Concept of Wi-Fi network

Course Details

Course Duration : 120 Hours (30 days)
Training will be taken in two phases :
  • Phase 1 :
    • Machine Learning using Python
  • Phase 2 :
    • Embedded System Design using PIC MCUs
Start Date : 20th July, 2020
Contact Person : Dr. Prakash(prakash.r@vit.ac.in) / Dr. Sasikumar P(sasikumar.p@vit.ac.in)
Enquiry Contact : +91638344710 / +919442027059

Training Certificate


* Certificate from IBM EdTech Partner  

 * Certificate from Microchip on PIC platform

Course Outcomes


* Participants will learn Python programming from basics.  

 * Participants will learn different Machine Learning algorithms and their applications.

* Participants will be introduced to PIC18 microcontrollers and MPLABX software.  

 * The training program includes hands-on labs on Machine Learning as well as on MPLABX resulting in exploration of concepts.  

 * Participants shall become ready to solve challenges from an industry-based perspective. 

Training Fees

₹5000/- per student
(inclusive of GST)