Filter machine operator education requirements

1. Input Filter Design

This course can also be taken for academic credit as ECEA 5707, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #3 in the Modeling and Control of Power Electronics course sequence. After completion of this course, you will gain an understanding of issues related to electromagnetic interference (EMI) and electromagnetic compatibility (EMC), the need for input filters and the effects input filters may have on converter responses. You will be able to design...

2. Filtering Explores with LookML

This is a Google Cloud Self-Paced Lab. In this lab, you will learn how to filter explores with LookML...

3. Autonomous Robots: Kalman Filter

4.5

(423)

Autonomous Robots are the future. Learn a core skill needed to create a Self-Driving Car!...

4. Data Fusion with Linear Kalman Filter

4.4

(461)

Theory and Implementation...

5. Visualizing Filters of a CNN using TensorFlow

In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework. The project uses the Google Colab environment which is a fantastic tool for creating and running Jupyter...

6. Image Segmentation, Filtering, and Region Analysis

In this course, you will build on the skills learned in Introduction to Image Processing to work through common complications such as noise. You’ll use spatial filters to deal with different types of artifacts. You’ll learn new approaches to segmentation such as edge detection and clustering. You’ll also analyze regions of interest and calculate properties such as size, orientation, and location. By the end of this course, you’ll be able to separate and analyze regions in your own images...

7. Advanced Kalman Filtering and Sensor Fusion

4.7

(486)

Theory and C++ Simulation Implementation for Autonomous Vehicles and Self Driving Cars!...

8. Analyze Huge Data with Ease Using Microsoft Excel Filters!

4

(365)

Learn the SECRETS of large scale data handling through the use of FILTERS! No more manual cutting and pasting!...

9. Packet Sniffing with Wireshark: Create Your First Filters

This guided project, Packet Sniffing with Wireshark: Create Your First Filters, will help an intermediate security analyst who is looking to use packet sniffing with Wireshark to capture, display, and observe specific HTTP and HTTPS packets. In this 1.5-hour long project-based course, you will learn how to use Wireshark for packet sniffing; to capture and observe certain network packets using display filters and capture filters. To achieve this, you will be taking on the role of helping an IT...

10. Tracking Objects in Video with Particle Filters

In this one hour long project-based course, you will tackle a real-world computer vision problem. We will be locating and tracking a target in a video shot with a digital camera. We will encounter some of the classic challenges that make computer vision difficult: noisy sensor data, objects that change shape, and occlusion (object hidden from view). We will tackle these challenges with an artificial intelligence technique called a particle filter. By the end of this project, you will have coded...

11. Water Treatment Process Design

4.4

(396)

design water treatment& RO/NF plants, sand& multimedia filter, iron& manganese removal filter, Ion exchange DI& softener...

12. Building Recommender Systems with Machine Learning and AI

4.5

(2,806)

How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python...

13. Robot Localization with Python and Particle Filters

In this one hour long project-based course, you will tackle a real-world problem in robotics. We will be simulating a robot that can move around in an unknown environment, and have it discover its own location using only a terrain map and an elevation sensor. We will encounter some of the classic challenges that make robotics difficult: noisy sensor data, and imprecise movement. We will tackle these challenges with an artificial intelligence technique called a particle filter. By the end of...

14. Digital Signal Processing (DSP) From Ground Up™ in C

4.3

(668)

Practical DSP in C : FFT, Filter Design, Convolution, IIR, FIR, Hamming Window, Linear Systems, Chebyshev filters etc...

15. Digital Signal Processing (DSP) From Ground Up™ in Python

4.2

(806)

Practical DSP in Python : Over 70 examples, FFT,Filter Design, IIR,FIR, Window Filters,Convolution,Linear Systems etc...

16. DSP From Ground Up™ on ARM Processors [UPDATED]

4.4

(386)

Digital Signal Processing on ARM : DFT, Filter Design, Convolution, IIR, FIR, CMSIS-DSP, Linear Systems...

17. 5 Machine Learning Projects from Dataisgood / Great Reviews

4

(711)

Learn Complete Machine Learning Bootcamp with Python. Build 5 Complete Machine Learning Real World Projects with Python...

18. Python Digital Image Processing From Ground Up™

4

(536)

Image Processing : Edge-Detection Algorithms , Convolution, Filter Design, Gray-Level Transformation, Histograms etc...

19. The Complete Machine Learning Course with Python

4.5

(6,881)

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!...