Cluster Analysis and Unsupervised Machine Learning in Python Practice Exam
Cluster Analysis and Unsupervised Machine Learning in Python Practice Exam
About Cluster Analysis and Unsupervised Machine Learning in Python Exam
The Cluster Analysis and Unsupervised Machine Learning in Python exam evaluates candidates' proficiency in applying unsupervised learning techniques, particularly clustering algorithms, to analyze data and gain insights without labeled data. It covers the implementation of common unsupervised learning algorithms such as K-Means, DBSCAN, Hierarchical Clustering, and others using Python-based libraries like Scikit-learn, NumPy, and Pandas. The exam will test the ability to handle data preprocessing, feature selection, and dimensionality reduction, along with the interpretation of clustering results and determining the optimal number of clusters. Candidates will also be assessed on evaluating the effectiveness of models using various metrics such as silhouette scores, Davies-Bouldin Index, and more.
Skills Required
- Solid understanding of unsupervised learning concepts and clustering techniques
- Proficiency in Python programming and relevant libraries (Scikit-learn, Pandas, NumPy)
- Experience in handling and preprocessing data for machine learning
- Knowledge of model evaluation techniques for clustering models
- Familiarity with dimensionality reduction techniques like PCA and t-SNE
- Ability to visualize and interpret data clustering results
Who should take the Exam?
- This exam is ideal for data scientists, machine learning engineers, and analysts who wish to enhance their expertise in unsupervised learning and clustering methods.
- It is also suitable for professionals seeking to apply these techniques to extract patterns from unlabelled datasets.
- Candidates should have a background in Python programming, basic data analysis, and machine learning concepts.
- Prior experience with supervised learning models and general data preprocessing will also be helpful for this exam.
- Whether you're looking to boost your career in data science or deepen your knowledge of unsupervised machine learning, this certification will demonstrate your skills in working with clustering algorithms and applying them to real-world data challenges.
Course Outline
The Cluster Analysis and Unsupervised Machine Learning in Python Exam covers the following topics -
Domain 1 - Getting Started
- Source Code Access
Domain 2 - Introduction to Unsupervised Learning
- Applications of Unsupervised Learning
- Why Clustering is Important
Domain 3 - K-Means Clustering
- Introduction to K-Means Clustering
- K-Means Algorithm Overview: Theory and Code
- Soft K-Means vs. Hard K-Means
- Visualizing K-Means Clustering Steps
- Common Pitfalls in K-Means Clustering
- Limitations of K-Means Clustering
- Evaluating Clustering Performance (Purity, Davies-Bouldin Index)
- Applying K-Means to Real Data (MNIST)
- Determining the Optimal Value of K
- K-Means for Word Clustering
- Real-World Applications of K-Means in NLP and Computer Vision
Domain 4 - Hierarchical Clustering
- Visual Overview of Agglomerative Hierarchical Clustering
- Exploring Agglomerative Clustering Options
- Implementing Hierarchical Clustering in Python and Interpreting Dendrograms
- Real-World Application: Evolutionary Analysis and Analyzing Tweets from Political Figures (Donald Trump vs. Hillary Clinton)
Domain 5 - Gaussian Mixture Models (GMM)
- Understanding Gaussian Mixture Model (GMM)
- Implementing GMM in Python
- Practical Challenges with GMM
- Comparing GMM and K-Means Clustering
- Introduction to Kernel Density Estimation
- GMM vs. Bayes Classifier (Part 1 & 2)
- Introduction to Expectation-Maximization (EM) Algorithm (Part 1)
- Expectation-Maximization (Part 2 & 3)
Domain 6 - Environment Setup (Appendix)
- Pre-Installation Requirements
- Setting Up Anaconda Environment
- Installing Numpy, Scipy, Matplotlib, Pandas, and TensorFlow
Domain 7 - Additional Help for Python Beginners (Appendix)
- Coding for Beginners (Part 1 & 2)
- Comparison of Jupyter Notebook Usage
- Using GitHub & Extra Coding Tips (Optional)
Domain 8 - Effective Learning Strategies for Machine Learning (Appendix)
- Tips for Success in this Course
- Is this Course for Beginners or Experts? Practical vs. Academic Focus
- Choosing the Right Course Order (Part 1 and 2)
