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4.2.2-coding-exercise-2 icon 4.2.2-coding-exercise-2

GTx: CS1301xIII Computing in Python III: Data Structures , Lesson 2: Declaring Strings in Python| 4.2.2 Coding Exercise 2

ibm-introduction-to-machine-learning-specialization icon ibm-introduction-to-machine-learning-specialization

This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science. After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices.

imageclassifier icon imageclassifier

Project code for Udacity's AI Programming with Python Nanodegree program. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.

open-contributions icon open-contributions

This Repository is for Learning purpose, and open contributions under DevIncept program.

python-3-programming-specialization icon python-3-programming-specialization

WHAT YOU WILL LEARN How to inspect and understand APIs and third party libraries to be used with Python 3 How to apply the Python imaging library (pillow) to open, view, and manipulate images, including cropping, resizing, recoloring, and overlaying text How to apply the python tesseract (py-tesseract) library with Python 3 in order to detect text in images through optical character recognition (OCR) How to apply the open source computer vision library (opencv) to detect faces in images, & how to crop and manipulate these faces into contact sheets

python-3-programming-specialization- icon python-3-programming-specialization-

This specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance.

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