In this video, we will cover probability model. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this video, we will cover Homework. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...

In this video, we will cover conditional probability total probability theorem. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning...

In this video, we will cover conditional probability in machine learning. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory...

In this video, we will cover regression.

In this video, we will cover joint distributions. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this video, we will cover a quick introduction to this section. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...

In this video, we will cover variance. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...

In this video, we will cover the curse of dimensionality. 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, we...

In this video, we will cover FashionMNIST example plan neural network. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...

In this video, we will cover machine learning model multi target models. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...

This video shows the process for solving the problem presented in IGCSE A June 2018 paper 2H Q13, which involves probability and tree diagrams. Students will learn how to construct a probability tree diagram to visualize outcomes of...

In this video we are going to look at the “and” and “or” rules of probability and what the term mutually exclusive means.

👉 Learn how to find probability from a normal distribution curve. A set of data are said to be normally distributed if the set of data is symmetrical about the mean. The shape of a normal distribution curve is bell-shaped. The normal...

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes....

In this video, we will cover continuous distributions - probability densities. This clip is from the chapter "Random Variables - Rationale and Applications" of the series "Probability / Statistics - The Foundations of Machine...

In this video, we will cover spam detection - implementation issues. This clip is from the chapter "Applications and Rules for Probability" of the series "Probability / Statistics - The Foundations of Machine Learning".In this section,...

This video explains Latin Hypercube stratified sampling. This clip is from the chapter "Perform Quantitative Risk Analysis" of the series "PMI-RMP® Certification Training".This section explains performing quantitative risk analysis.

This video introduces you to the Naive Bayes probability model. This clip is from the chapter "Naive Bayes Probability Model" of the series "Practical Data Science Using Python".This section explains Naive Bayes probability model –...

In this video, we will understand entropy. This clip is from the chapter "Classification Algorithms: Decision Tree" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will cover...

Like all dataset characteristics, we will look at the impurities in a dataset and how they should be minimum in a good dataset. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest...

This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition". This clip...

In this video, you will learn about weighted sums. This clip is from the chapter "Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will dive deeper into deep learning.

In this video, we will work on the problem of negative scores. This clip is from the chapter "Basics of Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will cover the...