Python Data Science: Unsupervised Machine Learning
This Course Includes:
6.5 hours of on-demand video: Comprehensive video lectures covering all aspects of unsupervised learning in Python.
18 downloadable resources: Supplemental materials to aid your learning.
Full lifetime access: Unlimited access to the course material.
Access on mobile and TV: Learn on the go with mobile access.
Certificate of completion: Proof of your achievement upon finishing the course.
What You'll Learn:
Foundations of Unsupervised Machine Learning:
- Introduction to unsupervised learning and its importance in data science.
- Understanding key concepts like clustering, anomaly detection, dimensionality reduction, and recommendation systems.
Data Preparation:
- Techniques for preparing data for modeling, including feature engineering, selection, and scaling.
- Methods for setting the correct row granularity, applying feature engineering, selecting relevant features, and scaling data using normalization and standardization.
Clustering Algorithms:
- K-Means Clustering:
- How to fit, tune, and interpret K-Means models.
- Techniques for interpreting cluster centers and using inertia plots to select the number of clusters.
- Hierarchical Clustering:
- Using dendrograms to identify clusters.
- Interpreting results with cluster maps.
- DBSCAN:
- Detecting clusters and noise points.
- Evaluating models using silhouette scores.
- Anomaly Detection:
- Application of techniques like Isolation Forests and DBSCAN.
- Tuning and interpreting results to identify outliers and anomalous patterns.
- Visualizing anomalies using pair plots.
- Dimensionality Reduction:
- Principal Component Analysis (PCA):
- Benefits for feature extraction and data visualization.
- Applying PCA in data science workflows.
- t-SNE:
- Ideal for data visualization.
- Practical applications and interpretation of results.
- Recommendation Engines:
- Building recommendation systems using content-based and collaborative filtering techniques.
- Techniques like Cosine Similarity and Singular Value Decomposition (SVD).
- Practical project: Creating recommenders for employee retention in an HR analytics context.
Description:
This hands-on, project-based course helps you master unsupervised machine learning in Python. The course begins with an overview of the Python data science workflow, including techniques and applications of unsupervised learning. You will learn how to prepare data for modeling, fit and tune various clustering models, apply anomaly detection techniques, and use dimensionality reduction methods. Additionally, you'll build recommendation engines and gain practical experience through real-world projects.
Why Should You Take This Course?
Comprehensive Curriculum:
- Covers all major aspects of unsupervised learning.
Practical Projects:
- Hands-on projects provide real-world experience.
Expert Instructors:
- Taught by experienced professionals from Maven Analytics and Alice Zhao.
Positive Feedback:
- High ratings and positive reviews from students.
Lifetime Access:
- Ongoing access to course materials and updates.
There Are a Ton of Open Jobs:
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Business Analyst
- Research Scientist
Who This Course Is For:
Aspiring Data Scientists:
- Those looking to gain practical experience with unsupervised learning techniques.
Professionals:
- Individuals wanting to enhance their data science skills.
Students:
- Those interested in applying machine learning techniques to real-world problems.
Beginners to Intermediate Learners:
- Anyone with a basic understanding of Python and data science concepts.
This course is structured to benefit anyone with basic programming knowledge, whether you're starting your career or looking to enhance your skills for more advanced roles.
RAR password:
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