Dimensionality Reduction and Manifold Learning - Applied Machine Learning in Python

Link to this course:
https://click.linksynergy.com/deeplink?id=Gw/ETjJoU9M&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fpython-machine-learning
Dimensionality Reduction and Manifold Learning - Applied Machine Learning in Python
Applied Data Science with Python Specialization
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Python Programming, Machine Learning (ML) Algorithms, Machine Learning, Scikit-Learn
The course was really interesting to go through. All the related assignments whether be Quizzes or the Hands-On really test the knowledge. Kudos to the mentor for teaching us in in such a lucid way.,This is an excellent course. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material. Also, the forums are pretty interactive.
This module 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.
Dimensionality Reduction and Manifold Learning - Applied Machine Learning in Python
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