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Why Learn Machine Learning?
Machine Learning is revolutionizing the way we interpret data and make predictions, offering an exciting opportunity to build intelligent systems that learn and adapt to new information.
Learn the foundational concepts of machine learning, including supervised, unsupervised, and reinforcement learning. The lesson covers the basic principles, types of learning, and common algorithms used in the field.
Data Preprocessing Techniques:
Discover essential data preprocessing techniques to prepare data for machine learning models. The lesson includes data cleaning, normalization, and feature extraction to ensure high-quality input for model training.
Implementing Regression Algorithms:
Explore how to implement regression algorithms to predict continuous outcomes. The lesson covers linear regression, polynomial regression, and performance evaluation metrics like R-squared and Mean Squared Error (MSE).
Classification Techniques and Models:
Learn about classification techniques for predicting categorical outcomes. The lesson includes algorithms such as logistic regression, decision trees, and support vector machines, along with metrics like accuracy and confusion matrices.
Clustering and Unsupervised Learning:
Understand unsupervised learning methods for grouping similar data points. The lesson covers clustering algorithms like K-means, hierarchical clustering, and evaluating cluster quality.
Model Selection and Evaluation:
Learn how to select and evaluate machine learning models to ensure they perform well on unseen data. The lesson includes cross-validation, model comparison, and metrics for assessing model effectiveness.
Feature Engineering and Selection:
Discover techniques for feature engineering and selection to enhance model performance. The lesson covers creating new features, selecting important features, and dimensionality reduction methods.
Implementing Neural Networks:
Explore the basics of neural networks and their application in machine learning. The lesson includes building simple neural networks, understanding activation functions, and using frameworks like TensorFlow or PyTorch.
Hyperparameter Tuning and Optimization:
Learn techniques for tuning and optimizing hyperparameters to improve model performance. The lesson covers grid search, random search, and advanced optimization methods.
Deploying Machine Learning Models:
Discover how to deploy machine learning models into production environments. The lesson includes model serving, API integration, and monitoring model performance in real-world applications.
JOIN THE COURSE
Course Overview:
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Introduction to Machine Learning: |
March 19, 2025
by
M.Junaid Faheem
Explore the fundamentals of machine learning, including its definition, history, and key concepts.
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Supervised Learning: |
March 19, 2025
by
M.Junaid Faheem
Learn about supervised learning techniques, including regression and classification, and their applications.
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Unsupervised Learning: |
March 19, 2025
by
M.Junaid Faheem
Dive into unsupervised learning methods such as clustering and dimensionality reduction.
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Model Evaluation and Tuning: |
March 19, 2025
by
M.Junaid Faheem
Understand how to evaluate machine learning models and fine-tune their parameters for better performance.
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Deep Learning: |
March 19, 2025
by
M.Junaid Faheem
Explore the advanced topic of deep learning, including neural networks and their applications.
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Feature Engineering: |
March 19, 2025
by
M.Junaid Faheem
Learn techniques for feature extraction, transformation, and selection to improve model accuracy.
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Applications of Machine Learning: |
March 19, 2025
by
M.Junaid Faheem
Discover how machine learning is applied in various industries such as finance, healthcare, and technology.
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Ethics in Machine Learning: |
March 19, 2025
by
M.Junaid Faheem
Examine the ethical considerations and challenges in deploying machine learning models.
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Project Development: |
March 19, 2025
by
M.Junaid Faheem
Work on hands-on projects to apply machine learning concepts and techniques in real-world scenarios.
Class Venue
24 Hudson St, New York, NY 10014
Room 32
