Supervised and Unsupervised Learning
( 30 Modules )

Module #1
Introduction to Machine Learning
Overview of machine learning, types of learning, and applications

Module #2
Supervised vs Unsupervised Learning
Key differences between supervised and unsupervised learning

Module #3
Mathematical Preliminaries
Review of linear algebra, calculus, and probability theory

Module #4
Data Preprocessing
Importance of data preprocessing, techniques for feature scaling and normalization

Module #5
Model Evaluation Metrics
Overview of common evaluation metrics for supervised and unsupervised learning

Module #6
Linear Regression
Introduction to linear regression, ordinary least squares, and gradient descent

Module #7
Logistic Regression
Introduction to logistic regression, binary classification, and decision boundaries

Module #8
Decision Trees
Introduction to decision trees, entropy, and information gain

Module #9
Random Forests
Introduction to random forests, ensemble learning, and bootstrap sampling

Module #10
Support Vector Machines
Introduction to SVMs, kernel methods, and soft margin classification

Module #11
Neural Networks for Supervised Learning
Introduction to neural networks for supervised learning, backpropagation, and activation functions

Module #12
Model Selection and Hyperparameter Tuning
Importance of model selection and hyperparameter tuning, techniques for cross-validation and grid search

Module #13
K-Means Clustering
Introduction to k-means clustering, centroid initialization, and clustering evaluation metrics

Module #14
Hierarchical Clustering
Introduction to hierarchical clustering, dendrograms, and clustering evaluation metrics

Module #15
Principal Component Analysis
Introduction to PCA, dimensionality reduction, and feature extraction

Module #16
t-SNE and Dimensionality Reduction
Introduction to t-SNE, dimensionality reduction, and manifold learning

Module #17
Density-Based Clustering
Introduction to density-based clustering, DBSCAN, and noise detection

Module #18
Anomaly Detection
Introduction to anomaly detection, techniques for identifying outliers and novelties

Module #19
Deep Learning for Computer Vision
Introduction to deep learning for computer vision, convolutional neural networks, and transfer learning

Module #20
Autoencoders and Generative Models
Introduction to autoencoders, generative models, and unsupervised representation learning

Module #21
Word Embeddings and Natural Language Processing
Introduction to word embeddings, NLP, and text analysis

Module #22
Real-World Applications and Case Studies
Real-world applications and case studies of supervised and unsupervised learning

Module #23
Practical Implementation of Supervised Learning
Hands-on implementation of supervised learning algorithms using Python and scikit-learn

Module #24
Practical Implementation of Unsupervised Learning
Hands-on implementation of unsupervised learning algorithms using Python and scikit-learn

Module #25
Project Development and Deployment
Guided project development and deployment using supervised and unsupervised learning algorithms

Module #26
Practical Implementation of Deep Learning
Hands-on implementation of deep learning algorithms using Python and TensorFlow/Keras

Module #27
Practical Implementation of Natural Language Processing
Hands-on implementation of NLP algorithms using Python and spaCy

Module #28
Practical Implementation of Computer Vision
Hands-on implementation of computer vision algorithms using Python and OpenCV

Module #29
Big Data and Scalability
Practical implementation of supervised and unsupervised learning on big data using Spark and Hadoop

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Supervised and Unsupervised Learning career

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Supervised and Unsupervised Learning ( 30 Modules )

Supervised and Unsupervised Learning
( 30 Modules )