Machine Learning (unsupervised learning) Course Overview
The 'Machine Learning (Unsupervised Learning) Course Overview' offered by Open Source explores essential techniques for uncovering patterns in unlabeled data. This course is crucial for data scientists, analysts, and researchers seeking to enhance their skills in data exploration and insight generation, equipping them with the tools to drive innovation in various industries through advanced analytics.
Course outline & what you'll learn
- Definition and Importance of Unsupervised Learning
- Distinction between Supervised and Unsupervised Learning
- Data Cleaning and Preparation
- Handling Missing Values
- Feature Scaling and Normalization
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Evaluation of Clustering Techniques
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Autoencoders
- Statistical Approaches to Anomaly Detection
- Isolation Forests
- One-Class SVM
- Market Basket Analysis
- Apriori Algorithm
- FP-Growth Algorithm
- Customer Segmentation
- Image Compression
- Recommendation Systems
- Introduction to Python Libraries (scikit-learn, TensorFlow, etc.)
- Hands-on Projects using Real Datasets
- Real-world Applications of Unsupervised Learning
- Challenges and Limitations of Unsupervised Learning
- Emerging Trends in Unsupervised Learning
- Resources for Further Learning and Development
Why train with Traincrest
This Open Source course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.