AI & MACHINE LEARNING – FULL THEORETICAL COURSE скачать в хорошем качестве

AI & MACHINE LEARNING – FULL THEORETICAL COURSE

AI & MACHINE LEARNING – FULL THEORETICAL COURSE In this comprehensive Machine Learning course section, we explore the core techniques that power modern Artificial Intelligence. Starting with Supervised Learning, you will learn major regression algorithms such as Linear Regression, Polynomial Regression, Ridge, and Lasso, along with classification methods including Logistic Regression, k-Nearest Neighbors, Decision Trees, Random Forests, and Support Vector Machines. We then dive into Model Evaluation and Performance Metrics, understanding how models are measured using metrics like MSE, RMSE, MAE, R2R^2R2, Accuracy, Precision, Recall, F1-score, Confusion Matrix, ROC curves, and AUC, while examining the crucial concept of the bias–variance tradeoff. Next, we cover Unsupervised Learning, exploring clustering algorithms such as K-Means, Hierarchical Clustering, and DBSCAN, as well as dimensionality reduction methods like PCA, t-SNE, and Autoencoders. Association learning techniques such as the Apriori algorithm and market basket analysis are also introduced. The course then moves into Neural Networks and Deep Learning, explaining the artificial neuron model y=σ(wTx+b)y=\sigma(w^Tx+b)y=σ(wTx+b), activation functions (ReLU, Sigmoid, Tanh), loss functions, and the training process using forward propagation, backpropagation, and gradient descent optimization. You will also learn about Convolutional Neural Networks (CNNs) for image processing, Recurrent Neural Networks (RNNs) for sequential data such as time series and language modeling, and modern Natural Language Processing (NLP) techniques including word embeddings, attention mechanisms, transformers, and Large Language Models. The course concludes with Reinforcement Learning, covering agents, environments, policies, value functions, and Q-learning, along with real-world applications in robotics, gaming AI, and autonomous systems. Finally, we introduce Model Deployment and MLOps, including APIs, cloud deployment, monitoring, versioning, and optimization techniques such as quantization and pruning. This module provides a complete overview of modern machine learning techniques used in real-world AI systems. ________________________________________ Keywords machine learning, artificial intelligence, supervised learning, unsupervised learning, regression algorithms, linear regression, polynomial regression, ridge regression, lasso regression, classification algorithms, logistic regression, k nearest neighbors, decision trees, random forests, support vector machines, model evaluation metrics, MSE, RMSE, MAE, R squared, accuracy precision recall, F1 score, confusion matrix, ROC curve, AUC, bias variance tradeoff, clustering algorithms, k means clustering, hierarchical clustering, DBSCAN, dimensionality reduction, PCA, t-SNE, autoencoders, association learning, apriori algorithm, neural networks fundamentals, artificial neuron model, activation functions ReLU sigmoid tanh, deep learning, forward propagation, backpropagation, gradient descent, optimization algorithms SGD momentum Adam optimizer, regularization dropout batch normalization, convolutional neural networks CNN, image classification, object detection, recurrent neural networks RNN, LSTM, GRU, sequence models, natural language processing NLP, tokenization stemming lemmatization, bag of words, TF-IDF, word embeddings, attention mechanism, transformers, large language models LLM, reinforcement learning, Q learning, robotics AI, autonomous navigation, model deployment, MLOps, cloud AI deployment, edge AI, model monitoring, continuous training, model optimization, quantization, pruning. TIMESTAMPS: 00:00:00 INTRO 00:00:07 I- DATA PREPROCESSING & FEATURE ENGINEERING 00:34:36 II- SUPERVISED LEARNING 01:08:38 III- MODEL EVALUATION & PERFORMANCE METRICS 01:40:14 IV- UNSUPERVISED LEARNING 02:16:43 V- NEURAL NETWORKS FUNDAMENTALS 02:47:47 VI- DEEP LEARNING 03:19:51 VII- RECURRENT NEURAL NETWORKS (RNN) 03:42:10 VIII- NATURAL LANGUAGE PROCESSING (NLP) 04:02:09 IX- REINFORCEMENT LEARNING (RL)