Machine Learning and AI Concepts (with Animations)


Curriculum for the course Machine Learning and AI Concepts (with Animations)

Learn about all the most importnat concepts and terms related to machine learning and AI. Course developed by https://www.youtube.com/@turingtimemachine ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Contents ⭐️ 0:00:00 Introduction 0:00:31 Variance 0:00:58 Unsupervised Learning 0:01:11 Time Series Analysis 0:01:26 Transfer Learning 0:01:41 Gradient Descent 0:01:59 Stochastic Gradient Descent 0:02:12 Sentiment Analysis 0:02:24 Regression 0:02:33 Regularization 0:02:45 Logistic Regression 0:03:01 Linear Regression 0:03:20 Reinforcement Learning 0:03:33 Decision Trees 0:03:47 Random Forest 0:04:03 Truncation 0:04:16 Principal Component Analysis (PCA) 0:04:29 Pre-training 0:04:39 Object Detection 0:04:58 Oversampling 0:05:16 Outlier 0:05:28 Overfitting 0:05:44 One-Hot Encoding 0:05:57 Nearest Neighbor Search 0:06:09 Normal Distribution 0:06:18 Normalization 0:06:35 Natural Language Processing (NLP) 0:06:46 Matrix Factorization 0:06:58 Markov Chain 0:07:23 Model Selection 0:07:33 Model Evaluation 0:07:42 Jupyter Notebook 0:07:54 Knowledge Transfer 0:08:03 Knowledge Graphs 0:08:18 Joint Probability 0:08:28 Inductive Bias 0:08:41 Information Extraction 0:08:49 Inference 0:09:05 Imbalanced Data 0:09:15 Human in the Loop 0:09:30 Graphics Processing Unit (GPU) 0:09:41 Vanishing Gradient 0:09:55 Generalization 0:10:04 Generative Adversarial Networks (GANs) 0:10:19 Ensemble Methods 0:10:27 Multiclass Classification 0:10:38 Data Pre-processing 0:10:49 Regression Analysis 0:11:02 Sigmoid Function 0:11:13 Evolutionary Algorithms 0:11:24 Language Models 0:11:34 Backpropagation 0:11:46 Bagging 0:12:05 Dense Vector 0:12:19 Feature Engineering 0:12:29 Support Vector Machines (SVMs) 0:12:44 Cross-validation 0:13:15 Loss Function 0:13:29 P-value 0:13:47 T-test 0:13:57 Cosine Similarity 0:14:10 Dropout 0:14:21 Softmax Function 0:14:34 Bayes' Theorem 0:14:46 Tanh Function 0:14:57 ReLU Function (Rectified Linear Unit) 0:15:11 Mean Squared Error 0:15:22 Root Mean Square Error 0:15:35 R-squared 0:15:51 L1 and L2 Regularization 0:16:07 Learning Rate 0:16:36 Naive Bayes Classifier 0:16:48 Cost Function 0:17:00 Confusion Matrix 0:17:22 Precision 0:17:33 Recall 0:17:55 Area Under the Curve (AUC) 0:18:19 Train Test Split 0:18:40 Grid Search 0:19:17 Anomaly Detection 0:19:39 Missing Values 0:20:02 Euclidean Distance 0:20:19 Manhattan Distance 0:20:41 Hamming Distance 0:20:59 Jaccard Similarity 0:21:11 K-means Clustering 0:21:32 Bootstrapping 0:21:51 Hierarchical Clustering 0:22:04 Matrix Multiplication 0:22:22 Jacobian Matrix 0:22:37 Hessian Matrix 0:22:54 Measures of Central Tendency 0:23:20 Activation Function 0:23:34 Artificial Neural Network (ANN) 0:23:53 Perceptron 0:24:18 Convolutional Neural Network (CNN) 0:24:48 Recurrent Neural Network (RNN) 0:25:27 Long Short-Term Memory (LSTM) 0:25:52 Transformer Model 0:26:24 Padding 0:26:45 Pooling 0:27:01 Variational Autoencoder 0:27:26 Quantum Machine Learning 🎉 Thanks to our Champion and Sponsor supporters: 👾 Drake Milly 👾 Ulises Moralez 👾 Goddard Tan 👾 David MG 👾 Matthew Springman 👾 Claudio 👾 Oscar R. 👾 jedi-or-sith 👾 Nattira Maneerat 👾 Justin Hual -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

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