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Autonomous Cars Deep Learning And Computer Vision In Python

Course

AUTONOMOUS CARS DEEP LEARNING AND COMPUTER VISION IN PYTHON

Category

Python Online Courses

Eligibility

Working Professionals and Freshers

Mode

Regular Offline and Online Live Training

Batches

Week Days and Week Ends

Duration :

30 to 45 days

Python Objectives

•How to secure Python services.
•Students will learn widely used Python concepts
•You will learn basics of programming in Python
•Learn how to structure a large-scale project using Python.
•From A-Z: The Complete Beginners-Advanced Masterclass – Learn Python
•Learn to design and run complex automated workflows for Python
•This course will teach you how to get moving in Python.
•Understand Python and how to use it to write styles programmatically in Python.
•Learn the absolute basics about Python from scratch and take your skills to another level

autonomous cars deep learning and computer vision in python Course Highlights

•Post training offline support available
•Resume & Interviews Preparation Support
•We assist on Internship on Real-Time Project 
•Our Trainers with 15+ years of teaching Experience
•60+ Hours of Intensive Classroom & Online Sessions
•We provide you with your recorded session for further Reference
•We provide one to one mentorship for the students and Working Professionals
• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

Who are eligible for Python

•Big Data, E-commerce, Cloud Computing, Sap, Erp, Application Programming, Web Development
•Cognos Developer, Ab initio developer, Java Developers, .net Architects, Informatica, MSBI, Tivoli Monitoring, Oracle Apps functional and technical, change
•Java/J2EE, Springs, API, REST/, MySQL, Java, Admin UI developer with HTML/JavaScript/Ember.js, Java Enterprise Integration/ESB/API Management experts with Mule
•Php Developer, Web Designing, Telecaller, Web Designer, Css, Javascript, Ajax, Bootstrap, Mysql, Web Technologies, Web Development, Ui Developer
•Xml Publisher, Php Developer, Android Application Development, Html Tagging, E-publishing, Software Development

AUTONOMOUS CARS DEEP LEARNING AND COMPUTER VISION IN PYTHON Topics

Environment Setup and Installation
•Installation Notes: OpenCV3 and Python 3.7
•Install Anaconda, OpenCV, Tensorflow, and the Course Materials
•Test your Environment with Real-Time Edge Detection in a Jupyter Notebook
• 101: Getting the Most From This Course
•A Brief History of Autonomous Vehicles
•Course Overview and Learning Outcomes
•Python Crash Course [Optional]
•Python Basics: Whitespace, Imports, and Lists
•Python Basics: Tuples and Dictionaries
•Python Basics: Functions and Boolean Operations
•Python Basics: Looping and an Exercise
•Computer Vision Basics:
•What is computer vision and why is it important?
•Humans vs. Computers Vision system
•what is an image and how is it digitally stored?
•[Activity] View colored image and convert RGB to Gray
•[Activity] Detect lane lines in gray scale image
•[Activity] Detect lane lines in colored image
•What are the challenges of color selection technique?
•Color Spaces
•[Activity] Convert RGB to HSV color spaces and merge/split channels
•Convolutions – Sharpening and Blurring
•[Activity] Convolutions – Sharpening and Blurring
•Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)
•[Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)
•[Activity] Project #1: Canny Sobel and Laplace Edge Detection using Webcam
•Image Transformation – Rotations, Translation and Resizing
•[Activity] Code to perform rotation, translation and resizing
•Image Transformations – Perspective transform
•[Activity] Perform non-affine image transformation on a traffic sign image
•Image cropping dilation and erosion
•[Activity] Code to perform Image cropping dilation and erosion
•Region of interest masking
•[Activity] Code to define the region of interest
•Hough transform theory
•[Activity] Hough transform – practical example in python
•Project Solution: Hough transform to detect lane lines in an image
•Image Features and their importance for object detection
•[Activity] Find a truck in an image manually!
•Template Matching – Find a Truck
•[Activity] Project Solution: Find a Truck Using Template Matching
•Corner detection – Harris
•[Activity] Code to perform corner detection
•Image Scaling – Pyramiding up/down
•[Activity] Code to perform Image pyramiding
•Histogram of colors
•[Activity] Code to obtain color histogram
•Histogram of Oriented Gradients (HOG)
•[Activity] Code to perform HOG Feature extraction
•Feature Extraction – SIFT, SURF, FAST and ORB
•[Activity] FAST/ORB Feature Extraction in OpenCV
•Machine Learning:
•What is Machine Learning?
•Evaluating Machine Learning Systems with Cross-Validation
•Linear Regression
•[Activity] Linear Regression in Action
•Logistic Regression
•[Activity] Logistic Regression In Action
•Decision Trees and Random Forests
•[Activity] Decision Trees In Action
•Bayes Theorem and Naive Bayes
•[Activity] Naive Bayes in Action
•Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
•[Activity] Support Vector Classifiers in Action
•Project Solution: Detecting Cars Using SVM – Part #1
•[Activity] Detecting Cars Using SVM – Part #2
•[Activity] Project Solution: Detecting Cars Using SVM – Part #3
•Artificial Neural Networks
•Single Neuron Perceptron Model
•Activation Functions
•ANN Training and dataset split
•Practical Example – Vehicle Speed Determination
•Code to build a perceptron for binary classification
•Backpropagation Training
•Code to Train a perceptron for binary classification
•Two and Multi-layer Perceptron ANN
• – Build Multi-layer perceptron for binary classification
•Deep Learning and Tensorflow:
•Intro to Deep Learning and Tensorflow
•Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding.
•[Activity] Building a Logistic Classifier with Deep Learning and Keras
•ReLU Activation, and Preventing Overfitting with Dropout Regularlization
•[Activity] Improving our Classifier with Dropout Regularization
•Convolutional Neural Networks (CNN’s)
•Implementing CNN’s in Keras
•[Activity] Classifying Images with a Simple CNN,
•Max Pooling
•[Activity] Improving our CNN’s Topology and with Max Pooling
•[Activity] Build a CNN to Classify Traffic Signs
•[Activity] Build a CNN to Classify Traffic Siigns
•Wrapping Up