Curated Video
Recommender Systems Complete Course Beginner to Advanced - Project 1: Song Recommendation System Using Content-Based Filtering: Missing Values
In this lesson, we will develop a new data frame for our content-based filtering for missing values.
Curated Video
Recommender Systems with Machine Learning - Section Overview
This video provides an overview of the section. This clip is from the chapter "Basic of Recommender Systems" of the series "Recommender Systems with Machine Learning".This section focuses on the basics of recommender systems.
Curated Video
A Practical Approach to Timeseries Forecasting Using Python - BiLSTM for Time Series Forecasting
This video talks about BiLSTM for time series forecasting. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using Python".This section...
Curated Video
A Practical Approach to Timeseries Forecasting Using Python - LSTM Parameter Change and Stacked LSTM
This video explains LSTM Parameter Change and Stacked LSTM. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using Python".This section...
Curated Video
A Practical Approach to Timeseries Forecasting Using Python - Underfitting and Overfitting
This video explains the concepts of underfitting and overfitting. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using Python".This...
Curated Video
A Practical Approach to Timeseries Forecasting Using Python - Important Parameters
This video talks about the important parameters in time series forecasting. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using...
Packt
BiLSTM for Time Series Forecasting
This video talks about BiLSTM for time series forecasting. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using Python".This section...
Packt
LSTM Parameter Change and Stacked LSTM
This video explains LSTM Parameter Change and Stacked LSTM. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using Python".This section...
Packt
Underfitting and Overfitting
This video explains the concepts of underfitting and overfitting. This clip is from the chapter "Recurrent Neural Networks in Time Series Forecasting" of the series "A Practical Approach to Timeseries Forecasting Using Python".This...
Higgsino Physics
Machine Learning: Bias VS Variance
Bias versus variance trade-off, under fitting vs over fitting trade-off. Showing why there is balance between being underfit when using a machine learning model to perform better on a test set, instead only performing good on the...
Curated Video
Evaluate the accuracy of an artificial intelligence system : Pointers on Evaluating the Accuracy of Classification Modelling
From the section: Supervised Learning: Classification. In this section, the author talks about kNN- Classification, Naive Bayes Classification, SVM- Linear and Non-Linear Classification and also Gradient Boosting Machine (GBM)....
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : Evaluating Machine Learning Systems with Cross-Validation
From the section: Machine Learning: Part 1. In this section, we’ll learn how machine learning works, and how it fits in with the world of AI and deep learning. And learn to train, test and validate the data using K-fold cross-validation....
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : Decision Trees and Random Forests
From the section: Machine Learning: Part 1. In this section, we’ll learn how machine learning works, and how it fits in with the world of AI and deep learning. And learn to train, test and validate the data using K-fold cross-validation....
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Decision Trees In Action
From the section: Machine Learning: Part 1. In this section, we’ll learn how machine learning works, and how it fits in with the world of AI and deep learning. And learn to train, test and validate the data using K-fold cross-validation....
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
From the section: Machine Learning: Part 2. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data. Machine Learning: Part 2: Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : ReLU Activation, and Preventing Overfitting with Dropout Regularlization
From the section: Deep Learning and Tensorflow: Part 1. In this section, we’ll talk about what Deep Learning is, and how TensorFlow works at a low level. Deep Learning and Tensorflow: Part 1: ReLU Activation, and Preventing Overfitting...
Curated Video
Evaluate the impact of an AI application used in the real world. (case study) : Working with Flower Images: Case Study - Part 11
From the section: CNN-Industry Live Project: Playing With Real World Natural Images. This section includes a live project of working with flower images. CNN-Industry Live Project: Playing With Real World Natural Images: Working with...
Curated Video
Create a machine learning model of a real-life process or object : Improving the Network with Better Activation Functions and Dropout
From the section: Regression Task Airbnb Prices in New York. We will use a real-world Airbnb dataset that contains data about New York properties for rent in 2019 on Airbnb, including their price. It is a simple dataset and makes a good...
Institute for New Economic Thinking
Cosma Shalizi - Why Economics Needs Data Mining
Cosma Shalizi urges economists to stop doing what they are doing: Fitting large complex models to a small set of highly correlated time series data. Once you add enough variables, parameters, bells and whistles, your model can fit past...
Packt
Develop an AI system to solve a real-world problem : Training, Testing, and Validation
From the section: Predicting Sales with Supervised Learning. In this section, learners will use their first machine learning techniques, including Support Vector Machines and Artificial Neural Networks. These techniques will be applied...
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Improving our Classifier with Dropout Regularization
From the section: Deep Learning and Tensorflow: Part 1. In this section, we’ll talk about what Deep Learning is, and how TensorFlow works at a low level. Deep Learning and Tensorflow: Part 1: [Activity] Improving our Classifier with...
Curated Video
Machine Learning Random Forest with Python from Scratch - Pros and Cons of Random Forest
In this video, we will look at the benefits and limitations of Random Forest and the complexities involved in decision-making using Random Forest. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine...
Curated Video
Machine Learning Random Forest with Python from Scratch - Overfitting and Underfitting
In this video, you will learn about overfitting, a modeling error when a model performs well in training but not in testing, and underfitting, where the model neither performs well during training nor during testing. This clip is from...
Curated Video
Practical Data Science using Python - Decision Tree - Iris Dataset Case Study
This video explains decision tree - Iris Dataset case study. This clip is from the chapter "Classification using decision trees" of the series "Practical Data Science Using Python".This section explains classification using decision trees.