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via Udemy |
Go to Course: https://www.udemy.com/course/python-in-3-hours/
Certainly! Here's a detailed review and recommendation for the Coursera course based on the provided information: --- **Course Review and Recommendation: Python and Machine Learning Using Google Cloud** If you're a beginner eager to dive into Python programming and machine learning, and want an incredibly efficient, accessible, and risk-free learning experience, this course is an excellent choice. Taught by Dr. Mohammad H. Rafiei, a highly experienced researcher and instructor affiliated with Johns Hopkins University and Georgia State University, this course offers a compact yet thorough introduction to Python and machine learning—all in under 3 hours. **What I Love About This Course:** - **Accessibility and Convenience:** The course is entirely web-based, utilizing Google Colab, which means no software installation is necessary. All Python coding is done on free cloud resources (CPU, GPU, TPU), making it highly accessible regardless of your hardware. - **Time-Efficient:** In just less than 3 hours, you'll learn fundamental Python 3+ syntax, data processing with pandas and numpy, data visualization, and an introduction to neural networks and TensorFlow. It’s perfect for busy learners or those looking for a quick yet effective overview. - **Beginner-Friendly:** The course is tailored for those with little to no prior experience in Python or machine learning. It provides step-by-step guidance and practical exercises. - **Cost-Effective:** The course is backed by a 100% money-back guarantee, so you can learn risk-free. - **Practical and Hands-On:** The course emphasizes applying machine learning concepts through TensorFlow, enabling learners to develop regression and classification neural networks efficiently. **Who Should Take This Course:** - Individuals interested in learning Python from scratch without the hassle of software installations. - Students and professionals who want to develop machine learning models quickly on free Google cloud resources. - Busy learners who need an intensive, condensed introduction to Python and machine learning. - Those with some programming background in other languages who want to transition to Python. - Anyone with limited computer hardware but a decent internet connection and basic familiarity with computing concepts. **What Could Be Improved:** While the course covers a wide array of topics efficiently, it is primarily an introduction. For learners interested in deepening their understanding of machine learning or neural networks, supplementary advanced courses may be necessary. Additionally, more real-world project examples could enhance practical understanding. **Final Verdict:** This course is a fantastic starting point for absolute beginners and busy professionals who want to quickly acquire the skills necessary to start coding in Python and explore machine learning with no software setup hassle. The instructor’s expertise and the structured curriculum make it highly recommended for those looking to jumpstart their machine learning journey efficiently. --- **Recommendation:** I highly recommend this course to anyone looking to learn Python and machine learning in a practical, time-efficient way. Share this course with colleagues, friends, or students interested in AI and data science. It's an excellent investment for those who need quick, reliable, and accessible training—plus, with the money-back guarantee, there’s no risk involved! --- Would you like me to tailor this review further for a specific platform or audience?
1.1. Course instructor-----------------------------My name is Mohammad H. Rafiei, Ph.D. I am a researcher and instructor at Johns Hopkins University, College of Engineering, and Georgia State University, Department of Computer Science. I am also the founder of MHR Group LLC in Georgia, a data-analytic company, where we work with various domestic and global researchers at different institutions to address persistent challenges in Computer Science, Engineering, and Medicine, using state of the art machine learning and optimization techniques.It is my great pleasure to serve as a Udemy instructor, helping thousands of students and researchers across the globe to learn Python and machine learning.1.2. Does this course suit you?-----------------------------You want to (1) learn Python, (2) learn and apply machine learning artificial intelligence in Python, (3) run Python on free CPU, GPU, and TPU cloud computers, (4) do not want to install any bulky software on your computer, (5) want to do all this in less than 3 hours, (6) and want this course to be 100% moneyback guaranteed. If that is the case, then you are in the right place!In less than 3 hours, this course will teach you:Python 3+ from scratch (no installation is required; all on free cloud computers at Google)General machine learning concepts and neural networksHow to develop machine learning models using TensorFlow in Python 3+How to investigate your problems in PythonThis course helps you if:You are a Python beginner who is interested in learning Python and using Python to develop machine learning models in less than 3 hours.You are interested in using free and powerful cloud CPU and GPU computers to develop and run your Python codes.Almost wherever you are in the world, Google will give you free remote access to its computers.Free CPU, GPU, and TPU processors to develop and run your Python codes for Free!You only need to have Gmail (free) and Google Chrome (also free) installed on your operating system!It does not matter what your operating system is.No bulky software is required, just Google Chrome web browser!Almost all the cheapest computers in the market can handle Google Chrome, so no significant computer system is required.You have no or little knowledge of Python, are interested in learning Python and want to practice machine learning problems in Python, all in a matter of fewer than 3 hours.You might have no or little knowledge of Python; you will be taught!You might have no or little knowledge of machine learning or neural networks; you will be taught, and you will practice them in Python!You are so busy and don't have the time to go over a 25-hour course that teaches you many rudimentary programming basics.You need optimum materials in a minimum amount of time to help you drive Python by yourself!You prefer not even install any additional complicated software, editors, or programs on your computer to run Python!You might have an old rusty computer, but it is able to run the latest version of Google Chrome (i.e., the Google free web browser).Your computer has limited memory to run programming scripts or has a limited hard drive to install bulky and complicated software.You will benefit the most if you are familiar with at least one computation-based programming language, such as MATLAB, R, C, C++, C#, etc., and want to switch to or learn Python.We are not going to explain, say, what a "for-loop" is, but we will see how to create, say, "for-loops" in Python.We are not explaining what an array or matrix is.1.4. Course Overview-----------------------------180 Minutes in 12 Lectures:Lecture 01: An Introduction to the Course ( < 18 minutes)Lecture 02: Gmail, Chrome, and Google Colab (~11 minutes)Lecture 03: Operations, Built-in Functions, and Data Types (~20 minutes)Lecture 04: Loops, Conditional Scripts, and Functions (~16 minutes)Lecture 05: Numpy and Pandas for Data Processing (~28 minutes)Lecture 06: Matplotlib and Seaborn for Data Visualizations (~10 minutes)Lecture 07: Data Repositories and Data Split in Machine Learning (~15 minutes)Lecture 08: Data Processing and Calibrations in Machine Learning (~13 minutes)Lecture 09: Brief Introduction to Neural Networks (~11 minutes)Lecture 10: TensorFlow Keras for Regression Neural Networks (~16 minutes)Lecture 11: TensorFlow Keras for Classification Neural Networks ( ~13 minutes)Lecture 12: Hit the Road on Your Own! ( ~9 minutes)1.5. Your Contribution-----------------------------Please write a review about this course; then, we can modify it and make it better. If you find this course interesting, please refer it to your friends and colleagues.1.6. Acknowledgment-----------------------------I want to thank my wife, Fatemeh, for all her support in developing this course. I want to thank my friend and brother, Ahmad Mohammadshirazi, a computer science Ph.D. student at Ohio State University, for helping me in the video editing of this course.