Advanced Unsupervised Machine Learning With Python
Advanced Unsupervised Machine Learning With Python
Machine learning and Python have become key industry drivers in the global job and opportunity market.
This Spotle.ai Masterclass, designed and delivered by the industry experts and Ivy League academic leaders, will help you become a machine learning expert.
You will learn the subject with lots of applications and coding using Python programming language in real life business scenarios.
We understand the value of your time. All Spotle.ai courses are compact and to the point.
So that you can learn quickly and give more time to applications building. Get ready for the experiential learning.
After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets.
Unsupervised Machine Learning involves finding patterns in datasets. The core of this course involves study of the following algorithms:
Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model
Dimension Reduction: Principal Component Analysis
Unlike many other courses, this course:
Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition.
Has a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorization
Has questions (programming and theory) and solutions that allow learners to get practice with the course material
The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).
What Will I Get ?
- Cluster analysis and different clustering techniques
- K-means clustering and Hierarchical clustering
- Principal Component Analysis and Factor Analysis as BONUS
- How and when to choose the right set of algorithms in real life problems.
- Clustering Algorithms: Hierarchical, DBSCAN, K Means, Gaussian Mixture Model
- Implementation of clustering algorithms and principal component analysis in Python
- Dimensions Reduction: Principal Component Analysis (PCA)
- Applications of clustering and PCA using real world data
Teaching Style and Resources:
Course includes many examples with plots and animations used to help students get a better understanding of the material
Course has many exercises with solutions (theoretical, Jupyter Notebook, and programming) to allow students to gain additional practice
All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.
Requirements
- Basic knowledge of Linear Algebra including vectors, matrices, transpose, matrix multiplications, linear spaces
- Basic knowledge of Probability and Statistics including mean, covariance, and normal distributions
- Ability to program in Python 3
- Ability to run Python 3 programs on local machine in Jupyter notebooks and command window
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