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AI Masterclass Overview: From Basics to Advanced Algorithms || #shorts ||MRL Tech Solutions [Video]

AI Masterclass Overview: From Basics to Advanced Algorithms || #shorts ||MRL Tech Solutions

Discover the world of artificial intelligence through comprehensive tutorials, practical implementations, and expert insights. Our channel covers a wide range of AI algorithms to help you understand and apply AI concepts effectively.

Machine Learning Algorithms :

Supervised Learning: Dive into algorithms like Linear Regression, Logistic Regression, Support Vector Machines, and Neural Networks.
Unsupervised Learning: Explore K-means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA).
Semi-supervised Learning: Learn how to combine labeled and unlabeled data for training.
Reinforcement Learning: Understand Q-Learning and Deep Q-Networks (DQNs).

Deep Learning Algorithms:

Convolutional Neural Networks (CNNs): Learn about image and video recognition.
Recurrent Neural Networks (RNNs): Discover applications for time series and natural language data.
Generative Adversarial Networks (GANs): Create realistic data samples with GANs.
Autoencoders: Master dimensionality reduction and denoising techniques.

Natural Language Processing (NLP) Algorithms:

Tokenization: Break text into manageable tokens.
Named Entity Recognition (NER): Identify entities like names, dates, and places.
Sentiment Analysis: Determine the sentiment expressed in text.
Transformers: Explore models like BERT and GPT-3 for various NLP tasks.
Optimization Algorithms:

Gradient Descent: Minimize loss functions in model training.
Genetic Algorithms: Optimize using natural selection principles.

Ensemble Learning:

Bagging: Reduce variance by combining multiple models. Example: Random Forest.
Boosting: Improve performance by correcting previous errors. Examples: AdaBoost, Gradient Boosting Machines (GBM).

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