Post Graduate Program in Computer Vision for Autonomous Vehicles
4.4 /5- Offered bySkill Lync
Post Graduate Program in Computer Vision for Autonomous Vehicles at Skill Lync Overview
Duration | 32 weeks |
Total fee | ₹1.50 Lakh |
Mode of learning | Online |
Credential | Certificate |
Post Graduate Program in Computer Vision for Autonomous Vehicles at Skill Lync Highlights
- Earn a certificate of completion from Skill Lync
- Learn directly from best in class 7 Industry Experts
- *Easy EMI payment option available
- Project based Learning with dedicated support 13 Comprehensive projects
- Learn tools like Tensor flow, Keras, Python OpenCV and ML & DL
- Prepare for roles like Computer vision/ ML Engineer, System Software Engineer-Autonomous Vehicle, Perceptive Engineer
Post Graduate Program in Computer Vision for Autonomous Vehicles at Skill Lync Course details
- Any student with a background in computer science or students with basic programming skills can enroll themselves in computer science certificate courses.
- Someone who has a flair for learning more about computer systems, network security and aspire to enter the world of cloud computing.
- Arrays
- Vision-based navigation
- Sensors - Wheel Encoders, IMU
- Introduction to Computer vision
- Differential drive robot basics
- 2D Visualization
- 3D Visualization
- This course focuses right from the basics of image processing techniques to in-depth concepts of 3D vision, computer architecture frameworks such as ResNet, Yolo, etc
- The course provides foundation in Linear algebra, Python and OpenCV, and takes a gradual approach to advanced concepts of image segmentation, stereo imaging, etc
- Gain knowledge in foundations of Linear algebra, Python and OpenCV, and take a gradual approach to advanced concepts of image segmentation, stereo imaging, etc
Post Graduate Program in Computer Vision for Autonomous Vehicles at Skill Lync Curriculum
Data Structures and Algorithms using JAVA
Introduction
Arrays, Strings, and Lists
Stacks and Queues
Trees
Heaps and Tries
Graphs & Algorithms
Sorting
Searching and Hashing
Greedy Algorithms
Divide and Conquer
Backtracking
Dynamic Programming
Core and Advanced Python Programming
Introduction to Python, Python Basics
Strings, Decision control statements
Repetition Statements and Console input-output
Lists, Tuples, Sets, Dictionary
Functions and Recursion, Functional Programming and Lambda functions
File Input-Output and Modules
Classes and objects
Exception Handling, Iterators and Generators
Data Analysis with Pandas
Numeric and Scientific Computing using Numpy
Graphical User Interfaces with Tkinter
Interacting with Databases
Machine Learning Fundamentals In Depth
Basics of Probability and Statistics
Basics of Machine Learning (ML) & Artificial intelligence(AI)
Supervised Learning - Prediction
Supervised Learning - Classification
Supervised Learning - Classification
Random forest & Model Evaluation
Supervised Learning - Classification
Supervised Learning - Classification
Unsupervised Learning - Kmeans
Unsupervised Learning - Hierarchical
Unsupervised Learning - PCA
Supervised Learning - Classification
Data Structures and Algorithms using Python
Introduction
Lists
Trees
Heaps and Tries
Graphs
Sorting
Searching and Hashing
Greedy Algorithms
Divide and Conquer
Backtracking
Dynamic Programming
Basics of Computer Vision using Python
Introduction to Computer Vision
Image processing
Edge Detection
Image Segmentation and features
Binary Image Operation
Shape of Objects
Motion
Matching & Tracking
Interest Point
Image Registration
Lens & Camera projection
SOTA ML based CV Techniques
Introduction to Camera Systems Using C++
Camera Construction
Camera Models
Camera Calibration
Projective Geometry
Stereo Vision
Camera Systems
Image Pre-Processing
Image Processing -1 (Transformations)
Image Processing -2
Image Processing -3
Image Processing - 4
Introduction to Embedded Systems
Introduction to Machine Learning Algorithms and their Implementation in Python
Introduction to Data Science and Programming Languages (Tools) for Data Science
Basics of Programming
Essential Python Libraries
Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff
Evaluation Metrics
Importing Data and Hands on Imported Data
Univariate and Multivariate Linear Regression
Principal Component Analysis
Logistic Regression and k-nearest Neighbor
Decision Tree and Random Forest
K-Mean and Hierarchical Clustering
Neural Network
Introduction to ROS/Github/Linux
Introduction to Autonomous vehicles
Introduction to Linux system
ROS Introduction
Programming Basics for Robotics
ROS File System Concepts
ROS Computation Graph Concepts -I
ROS Computation concepts and visualization
ROS for autonomous vehicles
Mobile Robotics: Basics
Mobile Robotics: Perception
Mobile Robotics: Localization and Mapping
Mobile Robotics: Path planning
Post Graduate Program in Computer Vision for Autonomous Vehicles at Skill Lync Entry Requirements
- Education: B.Tech/B.E. from any recognized university
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Post Graduate Program in Computer Vision for Autonomous Vehicles