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via Udemy |
Go to Course: https://www.udemy.com/course/data-science-in-python-unsupervised-learning/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on unsupervised machine learning in Python offered by Maven Analytics: --- **Course Review and Recommendation: Unsupervised Learning in Python by Maven Analytics** **Overview:** This hands-on, project-based course is an excellent resource for anyone looking to deepen their understanding of unsupervised machine learning techniques using Python. Designed for data science professionals and aspiring data scientists, it covers fundamental concepts and practical applications through real-world projects, making it a highly valuable addition to your learning portfolio. **Content & Structure:** The course meticulously guides learners through the entire unsupervised learning workflow, starting from data preparation to advanced techniques such as clustering, anomaly detection, dimensionality reduction, and recommendation systems. The structured outline ensures a comprehensive understanding of each topic, with ample opportunities for practical application through projects, homework, and quizzes. Key highlights include: - **Data Science Workflow & Data Preparation:** A solid foundation in cleaning, scaling, and engineering features for modeling. - **Clustering Techniques:** In-depth exploration of K-Means, Hierarchical Clustering, and DBSCAN, complete with interpretative methods like inertia plots, dendrograms, and silhouette scores. - **Anomaly Detection:** Practical application of Isolation Forests and DBSCAN, critical for identifying outliers. - **Dimensionality Reduction:** Use of PCA and t-SNE for visualization and feature extraction. - **Recommendation Engines:** Creation of content-based and collaborative filtering recommenders using cosine similarity and SVD. **Teaching Approach:** The course emphasizes hands-on learning, featuring numerous project files, downloadable resources, and real-world case studies. Learners are encouraged to apply their skills to a simulated HR analytics scenario, which makes the content relevant and immediately applicable. **Strengths:** - Clear, step-by-step instruction with practical demonstrations. - Extensive materials, including an ebook, project files, and solutions. - Opportunities to apply concepts through multiple projects and quizzes. - Supportive instructor and active community forum for questions. **Who Should Enroll:** This course is highly recommended for business intelligence professionals, data scientists, and anyone interested in mastering unsupervised learning techniques with Python. Whether you’re expanding your technical skillset or looking for a practical guide to interpretative modeling, this course provides comprehensive coverage. **Final Verdict:** If you want a practical, project-oriented course that balances theory with hands-on application, **this course by Maven Analytics is an excellent choice**. The focus on interpretation of models and real-world applications makes it particularly valuable for those aiming to leverage unsupervised learning techniques in their work. **Rating: ⭐️⭐️⭐️⭐️⭐️ (5/5)** --- **In conclusion:** Join this course if you're seeking an intensive, beginner-to-intermediate learning experience in unsupervised learning with a focus on Python. With lifetime access, expert support, and practical projects, you’ll gain both the knowledge and skills needed to implement powerful unsupervised learning models confidently. Happy learning!
This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python.We'll start by reviewing the Python data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You'll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We'll start with K-Means Clustering, learn to interpret the output's cluster centers, and use inertia plots to select the right number of clusters. Next, we'll cover Hierarchical Clustering, where we'll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we'll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We'll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You'll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we'll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We'll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we'll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.Throughout the course you'll play the role of an Associate Data Scientist for the HR Analytics team at a software company trying to increase employee retention. Using the skills you learn throughout the course, you'll use Python to segment the employees, visualize the clusters, and recommend next steps to increase retention.COURSE OUTLINE:Intro to Data Science in PythonIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowUnsupervised Learning 101Review the basics of unsupervised learning, including key concepts, types of techniques and applications, and its place in the data science workflowPre-Modeling Data PrepRecap the data prep steps required to apply unsupervised learning models, including restructuring data, engineering & scaling features, and moreClusteringApply three different clustering techniques in Python and learn to interpret their results using metrics, visualizations, and domain expertiseAnomaly DetectionUnderstand where anomaly detection fits in the data science workflow, and apply techniques like Isolation Forests and DBSCAN in PythonDimensionality ReductionUse techniques like Principal Component Analysis (PCA) and t-SNE in Python to reduce the number of features in a data set without losing informationRecommendersRecognize the variety of approaches for creating recommenders, then apply unsupervised learning techniques in Python, including Cosine Similarity and Singular Vector Decomposition (SVD)__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:16.5 hours of high-quality video22 homework assignments7 quizzes3 projectsPython Data Science: Unsupervised Learning ebook (350+ pages)Downloadable project files & solutionsExpert support and Q & A forum30-day Udemy satisfaction guaranteeIf you're a business intelligence professional or data scientist looking for a practical overview of unsupervised learning techniques in Python with a focus on interpretation, this is the course for you.Happy learning!-Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!See why our courses are among the TOP-RATED on Udemy:"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M."Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.