English | Size: 775 MB
Genre: eLearning
Machine Learning, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Markov Models
What you’ll learn
Explore the fundamental mathematical concepts of machine learning algorithms
Apply linear machine learning models to perform regression and classification
Utilize mixture models to group similar data items
Develop machine learning models for time-series data prediction
Design ensemble learning models using various machine learning algorithms
This Machine Learning course offers a comprehensive introduction to the core concepts, algorithms, and techniques that form the foundation of modern machine learning. Designed to focus on theory rather than hands-on coding, the course covers essential topics such as supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. Learners will explore how these algorithms work and gain a deep understanding of their applications across various domains.
The course emphasizes theoretical knowledge, providing a solid grounding in critical concepts such as model evaluation, bias-variance trade-offs, overfitting, underfitting, and regularization. Additionally, it covers essential mathematical foundations like linear algebra, probability, statistics, and optimization techniques, ensuring learners are equipped to grasp the inner workings of machine learning models.
Ideal for students, professionals, and enthusiasts with a basic understanding of mathematics and programming, this course is tailored for those looking to develop a strong conceptual understanding of machine learning without engaging in hands-on implementation. It serves as an excellent foundation for future learning and practical applications, enabling learners to assess model performance, interpret results, and understand the theoretical basis of machine learning solutions.
By the end of the course, participants will be well-prepared to dive deeper into machine learning or apply their knowledge in data-driven fields, without requiring programming or software usage.
Who this course is for:
Students, data scientists and engineers seeking to solve data-driven problems through predictive modeling
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