Quick Start with Python

This course is designed for beginners who want to start their journey with Python. You will learn the basics of Python, including data types, variables, operators, and how to use them in real-world scenarios. You will also learn how to use the IPython shell and Jupyter Notebook to write and execute Python code.

10 Labs

Your First Python LabStart

Data Types and ConversionStart

Comprehensive Python HelloStart

Effective Python Code CommentingStart

Python Math and Augmented AssignmentStart

Convert Hours to SecondsStart

Exploring IPython's Interactive Computing FeaturesStart

Conditional Statements in PythonStart

Python Data Structures FundamentalsStart

Variable Type ConversionStart

In this course, We will learn how to use scikit-learn to build predictive models from data. We will explore the basic concepts of machine learning and see how to use scikit-learn to solve supervised and unsupervised learning problems. We will also learn how to evaluate models, tune parameters, and avoid common pitfalls. We will work through examples of machine learning problems using real-world datasets.

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In this course, you will learn the basic concepts and syntax of TensorFlow 2, and how to use TensorFlow 2 to implement deep learning algorithms.

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In this course, you will learn the basics of OpenCV. You will learn how to read, write, and display images and videos. You will also learn how to draw different shapes on images and videos.

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Supervised learning. If you are hearing or reading this term for the first time, then it may be completely unclear what it means. Don't worry. In this lab, you will get a comprehensive understanding of supervised learning; and, in the next chapter of the experiment, you will learn to use supervised learning to complete data prediction.

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During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the following lessons, you will be exposed to: logistic regression, K-nearest neighbor algorithm, naive Bayes, support vector machine, perceptron and artificial neural network, decision tree and random forest, and bagging and boosting methods. The course will start with the principle of each of these methods. You are supposed to fully understand the implementat

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In this course, you will fully understand unsupervised learning and learn to use unsupervised learning to perform data clustering.

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In this course, you will learn the basic concepts of deep learning, including the basic principles of neural networks, the basic principles of TensorFlow, Keras and PyTorch, and the basic principles of linear regression, logistic regression, and multi-layer neural networks. You will also learn how to use TensorFlow, Keras and PyTorch to build a linear regression model, a logistic regression model, and a multi-layer neural network model.

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This course contains lots of labs for Machine Learning, each lab is a small Machine Learning project with detailed guidance and solutions. You can practice your Machine Learning skills by completing these labs, improve your coding skills, and learn how to write clean and efficient code.

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This course contains lots of challenges for Machine Learning, each challenge is a small Machine Learning project with detailed instructions and solutions. You can practice your Machine Learning skills by solving these challenges, improve your problem-solving skills, and learn how to write clean and efficient code.

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