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via Udemy |
Go to Course: https://www.udemy.com/course/practical-machine-learning-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on machine learning and deep learning, based on the provided details: --- **Course Review and Recommendation: Mastering Machine Learning & Deep Learning on Coursera** Are you a developer eager to dive into the world of artificial intelligence? Want to build practical machine learning and deep learning models with confidence? This Coursera course is an excellent starting point, especially for beginners, looking to develop real-world skills in AI and machine learning. **Course Highlights:** - **Hands-on Approach:** The course emphasizes practical learning through real-world examples such as image recognition, sentiment analysis, and fraud detection. Each example is independent, allowing learners to progress at their own pace and choose the topics of interest. - **Modern Tools and Frameworks:** Students will gain proficiency in industry-standard frameworks like TensorFlow 2/Keras, and will learn to utilize powerful, free cloud environments such as Google Colab. This setup facilitates easy experimentation and testing. - **Comprehensive Content:** The curriculum covers foundational topics like data analysis, visualization, model selection, data preparation, and model evaluation. Recent updates incorporate advanced topics like natural language processing with BERT, providing a well-rounded learning experience. - **Effective Teaching:** The instructor, Mr. Madhu, brings over 20 years of teaching and real-world machine learning development experience. His clear, jargon-free explanations, combined with neatly organized notebooks and code, make complex topics accessible. - **Updated Material:** The course is continuously refined, with recent updates including new mathematical foundations, Python quick start, and practical exercises for deep learning. The integration of Google Colab and beginner-friendly resources helps students learn efficiently and independently. **Student Feedback:** - Learners appreciate the clear, straightforward explanations and the structured step-by-step guides. - The use of Google Colab for exercises is highly praised for its convenience and ease of testing code. - The course’s practical exercises, especially in deep learning, are well-received for their clarity and application. - The instructor’s responsiveness and ongoing support are frequently highlighted as key benefits. **Who Should Enroll?** - Beginners seeking a gentle yet practical introduction to machine learning. - Developers looking to expand their AI toolkit with hands-on experience. - Data enthusiasts interested in applying models to real-world problems. - Anyone wanting to stay updated with the latest in AI techniques and tools. **Final Recommendation:** This course is highly recommended for anyone interested in building a solid foundation in machine learning and deep learning. Its practical emphasis, beginner-friendly approach, and constantly updated content make it an ideal choice for learners at all levels. Whether you are starting from scratch or brushing up your skills, the course offers valuable resources, expert guidance, and a supportive learning environment to help you succeed in the rapidly growing field of AI. --- Feel free to ask if you'd like a shorter summary or more specific aspects highlighted!
Are you a developer interested in building machine learning and deep learning models? Do you want to be proficient in the rapidly growing field of artificial intelligence? One of the fastest and easiest ways to learn these skills is by working through practical hands-on examples.LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years!In this course, you will work through several practical, machine learning examples, such as image recognition, sentiment analysis, fraud detection, and more. In the process, you will learn how to use modern frameworks, such as Tensorflow 2/Keras, NumPy, Pandas, and Matplotlib. You will also learn how use powerful and free development environments in the cloud, like Google Colab.Each example is independent and follows a consistent structure, so you can work through examples in any order. In each example, you will learn:The nature of the problemHow to analyze and visualize dataHow to choose a suitable modelHow to prepare data for training and testingHow to build, test, and improve a machine learning modelAnswers to common questionsWhat to do nextOf course, there are some required foundations you will need for each example. Foundation sections are presented as needed. You can learn what interests you, in the order you want to learn it, on your own schedule.Why choose me as your instructor?Practical experience. I actively develop real world machine learning systems. I bring that experience to each course.Teaching experience. I've been writing and teaching for over 20 years.Commitment to quality. I am constantly updating my courses with improvements and new material. Ongoing support. Ask me anything! I'm here to help. I answer every question or concern promptly.Selected Reviewsclear explanations..to the point and no jargon..neat presentation of notebooks with codes..it's a step by step guide on creating machine learning models using Google colab..the models explained here are basic and thus perfect for beginners ,to understand how machine learning models are created based on the given problem and about techniques used to improve the accuracy..with the resources shared and Mr.Madhu's immediate response to messages/QA,one can learn more about a topic..highly recommended to all machine learning enthusiasts. - Ashraf UIThe cours is easy to understand and well presented, same thing for the practical examples Using google colab was a very good idea to present the course and to do the exercices , we can easily test a function or a line of code. The last three sections are very intresting, they are practical exercices for deep learning well presented and commented - Iheb GANDOUZThe way it is explained is really cool. I used to be bored after an hour during lectures, but the guide somehow makes it very interesting..- Anu Priya JJanuary 2020 updates:New mathematics and machine learning foundation section includingLogistic regression, loss and cost functions, gradient descent, and backpropagationAll examples updated to use Tensorflow 2 (Tensorflow 1 examples are available also)Jupyter note introductionPython quick startBasic linear algebraMarch 2020 updates:A sentiment and natural language processing sectionThis includes a modern BERT classification model with surprisingly high accuracyApril/May 2020 updates:Numerous assignment improvements, e.g. self-paced or guided approachAdd lectures on Google Colab, Python quick start, classify your own images and more!