Learn Fundamentals of Programming in Machine Learning >>>>>>> Free Machine Learning
Free Udemy Machine Learning
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Learn Fundamentals of Programming in Machine Learning |
Description
Machine Learning (ML) is an indispensable tool in today's data-driven world, providing insights and making informed decisions. This course aims to equip students with the fundamental knowledge and practical skills needed to navigate the exciting realm of ML. The course covers core concepts of ML, including its types, applications, workflow, and challenges. It delves into various supervised learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and naive Bayes, and their implementation, evaluation, and application in real-world scenarios. Unsupervised learning techniques, such as clustering algorithms like K-means and dimensionality reduction methods, are also covered. Cross-validation, a crucial aspect of model evaluation, is explored in-depth, and bias-variance tradeoff and regularization techniques are introduced. Ensemble methods, such as random forests and gradient boosting, are explored to improve overall accuracy and robustness. The course also introduces the world of deep learning and neural networks, focusing on convolutional neural networks (CNNs) and their applications in computer vision and image recognition. The training process for neural networks is also discussed, and techniques used to optimize their performance are explored. By the end of the course, students will have a solid foundation in ML and be well-equipped to tackle data-driven challenges in various domains.
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