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Learn about all the most important 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

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