Packt
Fundamentals of Neural Networks - Course Outline
This video explains the course outline and what the course has to offer. This clip is from the chapter "Welcome" of the series "Fundamentals in Neural Networks".This section introduces you to the course and the course outline.
Packt
Fundamentals of Neural Networks - Convolutional Operation
The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature...
Packt
Fundamentals of Neural Networks - Bi-Directional RNN
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the...
Packt
Fundamentals of Neural Networks - Backward Propagation
This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...
Curated Video
Fundamentals of Machine Learning - Sampling and Bootstrap
This video explains the two most famous and common procedures—cross-validation and Bootstrap. This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of statistical...
Curated Video
Fundamentals of Machine Learning - ROCAUC
In this final lab, you will learn about ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve). This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab...
Curated Video
Fundamentals of Machine Learning - Random Forests
This video explains a lab session on random forests and a review of decision trees. This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab exercises on linear...
Curated Video
Fundamentals of Machine Learning - Multilayer Perceptron (MLP)
This video explains a lab session on neural networks and Multilayer Perceptron (MLP) models. This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab exercises on linear...
Curated Video
Fundamentals of Machine Learning - Model Selection
This video explains model selection and regularization. This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of statistical learning, sampling, and Bootstrap as well...
Curated Video
Fundamentals of Machine Learning - Introduction
This video explains a brief history of where machine learning started. This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of statistical learning, sampling, and...
Curated Video
Fundamentals of Machine Learning - Deep Learning
This video introduces you to deep learning, artificial neural networks, recurrent neural networks, and more. This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of...
Curated Video
Fundamentals of Machine Learning - CNN
This video continues the lab from neural networks and will have a look at convolutional neural networks. This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Derived Features
In this video, we will cover derived features. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
In this video, we will cover an introduction to Scikit-Learn. This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Overfitting Introduction
In this video, we will cover an introduction to overfitting. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section,...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
In this video, we will cover Scikit-Learn for SVM and random forests. This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory and...
Curated Video
The Complete Excel Guide: Beginners to Advanced - Statistical Functions for Forecasting - Part 2
The aim of this video is to explore more statistical functions for forecasting. This clip is from the chapter "Excel 2019 Advanced: Statistical Functions" of the series "The Complete Excel Guide: Beginners to Advanced".In this section,...
Curated Video
Statistics for Data Science and Business Analysis - OLS Assumptions
This video explains OLS assumptions in detail. This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section explains OLS assumptions, A1....
Curated Video
Statistics for Data Science and Business Analysis - A1. Linearity
In this video, the first assumption of OLS, linearity, is explained. This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section explains OLS...
Curated Video
Statistical Regression Models and Predicting Values
This video discusses how to determine the best statistical regression model to approximate data within a scatter plot and make predictions through interpolation and extrapolation. It covers the process of inputting data into a graphing...
Curated Video
Exploring the Results of Bivariate Data
In this video, the teacher explains the concept of residuals and how they can be used to assess the appropriateness of a linear regression model for a given data set. The teacher provides examples and demonstrates how to calculate...
Fun Robotics
Working principle of Linear Regression
Describes the working principle of a simple linear regression algorithm with an example.
Curated Video
Solving Problems Using Linear Regression
In this lesson, students will learn how to use statistics to understand relationships between variables and solve problems using linear regression. They will explore real-world examples, such as the correlation between team batting...