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#AI & #ML Lecture 5 : Learning a Linear Classifier, Perceptron Learning & Hyperplanes, KNN, Neural
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Classroom Contents
Artificial Intelligence and Machine Learning Full Course with C# Examples
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- 1 #AI & #ML Lecture 1 : Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
- 2 #AI & #ML Lecture 2 : Introduction to Machine Learning & Decision Trees, Supervised Learning
- 3 #AI & #ML Lecture 3 : Practical Example of Decision Trees with C# and Accord.NET, Cross Validation
- 4 #AI & #ML Lecture 4 : Proper Model Training & Testing, KNN Algorithm & Practical Example, Accuracy
- 5 #AI & #ML Lecture 5 : Learning a Linear Classifier, Perceptron Learning & Hyperplanes, KNN, Neural
- 6 #AI & #ML Lecture 6 : A Real Full Practical Example of How to Do Multilayer Perceptron Training 1/2
- 7 #AI & #ML Lecture 7 : A Real Full Practical Example of How to Do Multilayer Perceptron Training 2/2
- 8 #AI & #ML Lecture 8: Feature Selection & Normalization, Data Pre-Processing, TF-IDF, Text Processing
- 9 #AI & #ML Lecture 9 : Supervised Evaluation, K-Fold Cross Validation & Multiclass Classification
- 10 #AI & #ML Lecture 10: What Is Learning To Rank (LTR), Pointwise, Pairwise, and Listwise Ranking
- 11 #AI & #ML Lecture 11 : Gradient Descent, Loss Function, Sparse & Missing Data, Regularization, L1 L2
- 12 #AI & #ML Lecture 12 : Large & Soft Margin Classifiers, Support Vector Machines (SVM), Loss Function
- 13 #AI & #ML Lecture 13: Conditional Probability & Probabilistic Models, Joint Distribution, Random Var
- 14 #AI & #ML Lecture 14: Logistic Regression & Ensemble Learning - Bagging & Boosting - AdaBoost
- 15 #AI & #ML Lecture 15: Unsupervised Learning, Clustering Algorithms, Hierarchical Clustering, K-Means
- 16 How to do Free Speech-to-Text Transcription Better Than Google Premium API with OpenAI Whisper Model
