MACHINE LEARNING
Machine Learning is a technique behind innovations such as facial recognition, self-driving cars, predictive results in search, Predictive text etc. It is playing a significant role in developing new algorithms which permits software application to predict/achieve accurate results without being explicitly programmed. Machine Learning is implemented where programming or developing an efficient algorithm is problematic or not feasible. Machine Learning is revolutionizing the computing ecosystem. The objective of this training program is to understand how machines can learn from its past experience which is considered as the core of AI. We focus on creating strong fundamental knowledge sets on building algorithms by mastering the principles of statistical analysis.
1) Basics of machine learning terminology and concepts.
2) Overview of supervised, unsupervised, and reinforcement learning.
3) Introduction to data preprocessing, feature engineering, and model evaluation.
1) In-depth study of popular machine learning algorithms like decision trees, support vector machines, and neural networks.
2) Focus on model performance optimization, hyperparameter tuning, and cross-validation.
3) Introduction to advanced topics like ensemble methods and dimensionality reduction.
1) Deep dive into neural networks and deep learning architectures.
2) Specialized techniques such as transfer learning and generative adversarial networks (GANs).
3) Ethical considerations in machine learning, bias mitigation, and responsible AI practices.
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