Learn to build a music recommendation system using the k-means algorithm. We will use the audio features from the million song data and cluster them based on their similarities. In this blog, we will be discussing these topics: 1) Methods to build a recommendation system for songs 2) Step-wise implementation 3) Ordering songs for the recommendation, etc.
In this blog, we will build an image data compressor using unsupervised learning technique, Process Component Analysis. We will be discussing these topics: 1) Image types and quantization 2) PCA overview 3) Step-wise implementation of PCA for image compression. 4) Techniques to optimize the tradeoff between compression and the number of components.
Optimization of error function is the respiratory process for machine learning algorithms. But this error function varies for classification and regression problems. In this blog, we have discussed: 1) Definition and importance of loss function 2) Loss functions used for regression 3) Loss functions used for binary classification 4) Loss functions used for multiple classification, etc.
In this blog, we have discussed steps to master machine learning for implementing applications. Here are the steps: 1) Learn python 2) Learn math for ML 3) Learn concept of data, analysis, manipulation, filtering, and visualization and choose any open source dataset 5) Learn basics of ML and choose problem statement 6) Learn deep learning and neural networks.
Both classification and regression in machine learning deal with the problem of mapping a function from input to output. However, when it comes to classification problems, the output is a discrete (non-continuous) class label or categorical output. While on the other hand, when the problem is a regression problem, the output is continuous.
Pandas is a famous library package of Python used by data scientists and analysts for data understanding, data preprocessing, and much more. It provides us with numerous tools to do these manipulations and analysis efficiently. In this blog, We will cover installation and all the basic Pandas functions frequently used while building machine learning projects.