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applied-machine-learning-university-of-michigan-coursera-

Module-1 introduces basic Machine Learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library. Module-2 delves into a wider variety of supervised learning methods for both classification and regression. Module-3 covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models. Module-4 covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.

bootstrap4

An overview of client-side web UI frameworks, in particular Bootstrap 4. Grids and responsive design, Bootstrap CSS and JavaScript components. About CSS preprocessors, Less and Sass. Basics of Node.js and NPM and task runners like Grunt and Gulp.

coursera_capstone

IBM Coursera Capstone

data-collection-and-processing-with-python

This Course taught me to fetch and process data from services on the Internet. It covers Python list comprehensions processing deeply nested data. learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs.

deep-neural-networks-with-pytorch

Deep Neural Networks with PyTorch

get-started-python

A Python application and tutorial that use Flask framework to provide a REST API to receive requests from the UI. The API then persists the data to a Cloudant database.

getting-started-with-python

The basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python.

ibm-data-science-professional-certificate

Jupiter Notebooks with code, Assignments, Study Materials of IBM Data Science Professional Certificate.

ibm-ds-8-machine-learning-with-python

Basics of machine learning using an approachable, and well-known programming language, Python.A general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

intract-with-operating-system-using-python

Manipulation of files and processes on your computer’s operating system. Regular expressions-- a very powerful tool for processing text files. Execute Python locally, and organize and use code across different Python files. Read and write different types of files, and use sub processes and input streams. Bash scripting. Automatic testing. Set up your own developer environment in your machine.

introduction-to-deep-learning-neural-networks-with-keras

Introduction to Deep Learning & Neural Networks with Keras

introduction-to-github

khokhriya

Config files for my GitHub profile.

peer-graded-assignment-segmenting-and-clustering-neighborhoods-in-toronto

In this project, it is required to explore, segment, and cluster the neighborhoods in the city of Toronto. What is interesting about the field of data science is that each project can be challenging in its unique way, so we need to learn to be agile and refine the skill to learn new libraries and tools quickly depending on the project.

pillow-python

The Python Imaging Library, which is known as PIL or PILLOW, is the main library we use in python for dealing with image files.

python-classes-and-inheritance

This course introduces classes, instances, and inheritance. You will learn how to use classes to represent data in concise and natural ways. You'll also learn how to override built-in methods and how to create "inherited" classes that reuse functionality. You'll also learn about how to design classes. Finally, you will be introduced to the good programming habit of writing automated tests for their own code.

python-for-data-science-and-ai

This course took me from zero to programming in Python in a matter of hours—no prior programming experience necessary! I Learnt Python fundamentals, including data structures and data analysis, completing hands-on exercises throughout the course modules, and created a final project to demonstrate my new skills.

python-function-files-dictionaries

python-project-sentiment-classifier

In this project, the task is to build a sentiment classifier, which will detect how positive or negative each tweet is. We will create a csv file, which contains columns for the Number of Retweets, Number of Replies, Positive Score (which is how many happy words are in the tweet), Negative Score (which is how many angry words are in the tweet), and the Net Score for each tweet. At the end, we upload the csv file to Excel or Google Sheets, and produce a graph of the Net Score vs Number of Retweets.

python_developer_portfolio

Python_Developer_Portfolio hello dera

scalable-machine-learning-on-big-data-using-apache-spark

Scalable Machine Learning on Big Data using Apache Spark

web-application-technologies-and-django

In this course, you'll explore the basic structure of a web application, and how a web browser interacts with a web server. You'll be introduced to the Hypertext Transfer Protocol (HTTP) request/response cycle, including GET/POST/Redirect. You'll also gain an introductory understanding of Hypertext Markup Language (HTML), as well as the overall structure of a Django application. We will explore the Model-View-Controller (MVC) pattern for web applications and how it relates to Django. You will learn how to deploy a Django application using a service like PythonAnywhere so that it is available over the Internet.