In this video, we will have a quick introduction.
<
br/>
This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will cover the recommender...

In this video, you will learn how to implement K-Means clustering in Python.
<
br/>
This clip is from the chapter "Clustering" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, you will...

In this video, you will learn how to split data into train and test sets.
<
br/>
This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's...

In this video, you will learn how to split data into train and test sets.
<
br/>
This clip is from the chapter "Classification Algorithms: Decision Tree" of the series "Python for Machine Learning - The Complete Beginner's...

In this video, you will learn how to split data into train and test sets.
<
br/>
This clip is from the chapter "Classification Algorithms: K-Nearest Neighbors" of the series "Python for Machine Learning - The Complete Beginner's...

In this video, you will learn about results prediction and confusion matrix.
<
br/>
This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's...

In this video, you will learn about results prediction and accuracy.
<
br/>
This clip is from the chapter "Classification Algorithms: Decision Tree" of the series "Python for Machine Learning - The Complete Beginner's Course".In...

In this video, you will learn how to do pre-processing.
<
br/>
This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's Course".In this...

In this video, you will learn how to import the KNN classifier.
<
br/>
This clip is from the chapter "Classification Algorithms: K-Nearest Neighbors" of the series "Python for Machine Learning - The Complete Beginner's Course".In...

In this video, you will learn how to import the dataset.
<
br/>
This clip is from the chapter "Classification Algorithms: K-Nearest Neighbors" of the series "Python for Machine Learning - The Complete Beginner's Course".In this...

In this video, you will learn how to import libraries and datasets.
<
br/>
This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will...

In this video, you will learn how to import libraries and datasets.
<
br/>
This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's...

In this video, you will learn implementation in Python.<br<br/>/>

This clip is from the chapter "Clustering" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, you will learn about clustering.

In this video, you will learn how to grab the ratings for two movies.
<
br/>
This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will...

In this video, you will learn about data pre-processing.
<
br/>
This clip is from the chapter "Recommender System" of the series "Python for Machine Learning - The Complete Beginner's Course".In this section, we will cover the...

A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the...

This video explains recurrent neural networks and why we want to use RNN. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...

This video explains the need for taking up the course and introduces you to the author. This clip is from the chapter "Welcome" of the series "Fundamentals in Neural Networks".This section introduces you to the course and the course...

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...

For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. This clip is from the chapter "Convolutional Neural...

This video explains the purpose of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will learn every...

This video explains padding in convolutional neural networks. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...