Machine Learning (ML)
10 Paid Courses
60 Students
Embark on an exciting journey into the world of machine learning with our comprehensive course at Mizan Institute of Technology (MiT). Unlock the power of data-driven decision making and gain the skills to build innovative solutions that drive progress in various industries.
Over the course of 3 months, you'll dive deep into:
- Foundations of machine learning, including key concepts and applications
- Data cleaning and preprocessing techniques to ensure high-quality datasets
- Data transformation methods for improved model performance
- Regression analysis to model relationships and predict continuous outcomes
- Classification algorithms for categorizing data into predefined classes
- Clustering techniques to uncover hidden patterns and insights in data
- Dimensionality reduction methods to optimize model performance and visualization
- Evaluation metrics and cross-validation strategies to ensure robust models
- Hands-on experience in tackling real-world machine learning projects
Whether you're aiming to advance your career in data science, or looking to incorporate machine learning into your current role, this course provides the perfect foundation to turn your aspirations into reality. Join us at MiT and take the first step towards becoming a skilled machine learning practitioner, capable of driving innovation and solving complex problems.
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Paid
Gain an overview of machine learning, including its history, key concepts, and applications. This course provides a strong foundation in understanding how machine learning models are built and utilized in various industries.
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8 Sessions
- Week 1-2
- All levels
Paid
Understand how to transform data for better model performance, including normalization, scaling, and encoding categorical variables. These techniques are crucial for preparing data to be effectively used by machine learning algorithms.
Dive into regression analysis, focusing on linear regression and its variations. This course teaches how to model relationships between variables and predict continuous outcomes.
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8 Lessons
- Week 3-4
- All levels
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Explore classification algorithms such as logistic regression, decision trees, and support vector machines. This course covers techniques for categorizing data into predefined classes and evaluating classifier performance.
Learn about clustering methods like K-means and hierarchical clustering. This course focuses on grouping similar data points and understanding the underlying patterns within datasets.
Study techniques such as Principal Component Analysis (PCA) to reduce the number of features in a dataset while preserving important information. This course helps in improving model performance and visualizing high-dimensional data.
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16 Lessons
- Week 5-7
- All levels
Paid
Understand various performance metrics for regression, classification, and clustering models. This course covers accuracy, precision, recall, F1-score, and more, essential for evaluating model effectiveness.
Learn about cross-validation techniques to assess model performance and prevent overfitting. This course teaches how to use training and validation sets effectively to ensure robust models.
Apply all learned concepts in a comprehensive machine learning project. This course provides hands-on experience in tackling real-world problems, from data preprocessing to model deployment, consolidating your machine learning skills.
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20 Lessons
- Week 8-12
- All levels