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Description of  Mql5 Machine Learning 01 - Neural Networks For Algo-Trading

Published 12/2023 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.41 GB | Duration: 8h 13m A firm and steadfast introduction to Machine Learning and Neural network application in Algorithmic trading with MQL5 What you'll learn Introduction to Data science Introduction to Artificial intelligence Introduction to Machine learning Coding Neural networks in MQL5 Training Neural Networks in MQL5 Requirements MQL5 Beginner knowledge Description Overview Section 1: Overview of Machine learning Lecture 1 Data science, Artificial intelligence and Machine learning Lecture 2 Types of Machine learning Lecture 3 Introduction to Neural Networks Lecture 4 Feed Forward Neural Network Architecture Section 2: Introduction to Neural Networks Lecture 5 ForwardPass on a spreadsheet Lecture 6 Mean squared error on a spread sheet Lecture 7 Backward pass on a spread sheet Lecture 8 Gradient descent on a spread sheet Section 3: Vector and Matrix Datatypes Lecture 9 Linear Algebra, Vectors and Matrices Lecture 10 Declaring Matrices and Vectors Lecture 11 Initializing Matrices and Vectors Lecture 12 Copying Data into Matrices and Vectors Lecture 13 Copying Timeseries Data into Matrices and Vectors Lecture 14 Matrices and Vector Operations Lecture 15 Manipulating Matrices Section 4: Data Collection Lecture 16 Neural Network Architecture Lecture 17 General EA parameters Lecture 18 Setting the Live calculation interval Lecture 19 Creating Data Vessels Lecture 20 Initializing Handles Lecture 21 Collecting indicator Data Lecture 22 Data Normalization Lecture 23 Initializing Weights and Bias Section 5: Forward Pass Lecture 24 Converting Matrices to Vectors Lecture 25 Converting Vectors to Matrices Lecture 26 Neuron Calculations Lecture 27 Forward Function Section 6: Neural Network Training Lecture 28 Searching for Patterns Lecture 29 Removing an index from a Vector Lecture 30 Removing Matrix Rows and Columns Lecture 31 Confusion Matrix Declaration Lecture 32 Populating the Confusion Matrix Lecture 33 Model Accuracy and Precision Lecture 34 Recall / Sensitivity Calculation Lecture 35 Specificity calculation Lecture 36 F1 Score calculation Lecture 37 Support calculation Lecture 38 Predictive Metrics averages Lecture 39 Creating Data classes Lecture 40 One Hot Encoding Lecture 41 Loss Function Options Lecture 42 Batch Forward Pass Lecture 43 Back Propagation training Lecture 44 Prediction Presentation Lecture 45 Model Training Section 7: Model Testing Lecture 46 Displaying Probability Signals Lecture 47 Visually testing the model Lecture 48 Assignment Section 8: Conclusion Lecture 49 Conclusion Anyone wishing to use machine learning in algorithmic trading