Internet of Things (IOT) Syllabus
his Course focuses on hands-on IoT concepts such as sensing, actuation and communication. It covers the development of Internet of Things (IoT) prototypes—including devices for sensing, actuation, processing, and communication—to help you develop skills and experiences. The Internet of Things (IOT) is the next wave, world is going to witness. Today we live in an era of connected devices the future is of connected things.
Duration
navratri special discount offer
50%
Available Seats
30
Schedule
Weekly:- 5.00 pm - 7.00 pm
WeekEnd:- Saturday - Sunday : 11:00 am - 2:00 pm
1 Introduction to IOT
• Understanding IoT fundamentals
• IOT Architecture and protocols
• Various Platforms for IoT
• Real time Examples of IoT
• Overview of IoT components and IoT Communication Technologies
• Challenges in IOT
2 Arduino Simulation Environment
• Arduino Uno Architecture
• Setup the IDE, Writing Arduino Software
• Arduino Libraries
• Basics of Embedded C programming for Arduino
• Interfacing LED, push button and buzzer with Arduino
• Interfacing Arduino with LCD
3 Sensor & Actuators with Arduino
• Overview of Sensors working
• Analog and Digital Sensors
• Interfacing of Temperature, Humidity, Motion, Light and Gas Sensor with Arduino
• Interfacing of Actuators with Arduino.
• Interfacing of Relay Switch and Servo Motor with Arduino
4 Basic Networking with ESP8266 WiFi module
• Basics of Wireless Networking
• Introduction to ESP8266 Wi-Fi Module
• Various Wi-Fi library
• Web server- introduction, installation, configuration
• Posting sensor(s) data to web server
5 IoT Protocols
• M2M vs. IOT • Communication Protocols
6 Cloud Platforms for IOT
• Virtualization concepts and Cloud Architecture
• Cloud computing, benefits
• Cloud services -- SaaS, PaaS, IaaS
• Cloud providers & offerings
• Study of IOT Cloud platforms
• ThingSpeak API and MQTT
• Interfacing ESP8266 with Web services
7 Data Visualizations
7 a. Using Data visualization packages show data in graphical state
8 Data Analysis
a.In Data Analysis perform data extraction methods
9 Simple Multiple Linear Regression
a. Multiple Linear Regression ML algorithm
b. Examples and Case studies
10 Multiple Linear Regression
a. Learn Multiple Linear Regression ML algorithm
b. Examples and Case studies
11 Gradient Descent
a. Optimization Technique
b. Differential Mathematics
12 KNN
a. Learn KNN ML algorithm
b. Examples and Case studies
13 Model Performance Metrics
a. Performance calculation of ML algorithms
14 Model Selection Part1
a. Best ML model section technique
15 Naive Bayes
a. Learn Naive Bayes ML algorithm
b. Examples and Case studies
Logistic Regression
a. Learn Logistic Regression ML algorithm
b. Examples and Case studies
Support Vector Machine (SVM)
a. Learn Support Vector Machine (SVM) ML algorithm
b. Examples and Case studies
Decision Tree
a. Learn Decision Tree ML algorithm
b. Examples and Case studies
Ensembling
a. Ensembling Concepts
b. Examples and Case studies
Model Selection Part2
a. Best ML model section technique
Unsupervised Learning
a. Learn Kmeans ML algorithm.
b. Examples and Case studies
Dimension Reduction
a. ML Data dimensions reduction concepts Using PCA
Advanced Machine Learning Algorithms
a. Optimal Solution
b. Regularization
c. Ridge and Lasso
d. Model Selection
5.Deep Learning
a. Learn Neural Network ML algorithm
b. Examples and Case studies