Learn Machine Learning Maths Behind

Explore general machine learning algorithm steps, starting with naive Bayes' probabilistic framework, and dive into the math—learning parameters, differentiation, and integration—before implementing with Spark.

Demonstrates how we guess values, compute error, and iteratively refine with small learning-rate adjustments, minimizing error through parameter tuning, and applying linear regression and metric factorization math.

Explore the differences between supervised and unsupervised learning, and how a dataset guides learning, classification, and clustering, including identifying iPhones and forming k-means clusters from video feeds.

Examine how to begin, tune, and understand learning in machine learning, address local minima and oscillations, and apply collaborative filtering within a recommendation system.

Explore the mathematical side of machine learning through a practical example using P, Q, and K matrices. Learn about assumptions, feature values, and metrics that evaluate user–product interactions.

Explore how to measure and minimize the error between real metrics and cap values in sparse product-user feature matrices, using squared error and differentiation.

Minimize the squared error by differentiating the error function to find the minimum. The section covers real versus zero-initial, sparse metrics, and updating parameters in next iteration to reduce error.

Differentiate the error function to find its minimum, illustrated by a 2d or 3d space and the corresponding plane, and use the minimum error value to guide the next iteration.

Use differentiation to derive expressions for parameter updates, then incrementally refine p and q by comparing error values and slopes toward the minimum error, with a graphical interpretation.

Demonstrate how graphical metrics and errors drive iterative updates to model parameters, minimize error, and refine the learning process with regularization and Aitkin loss to prevent overfitting.

Discover the math behind machine learning by tracing p and q updates, gradient-like error minimization, and local minima through metrics factorization and iterative optimization in Spark.

Begin the hands-on machine learning workflow by building and training a distributed model with Spark on Google Cloud Platform using dataframes, training and test splits, and model evaluation.

Follow the hands-on handbook to spin up a Spark cluster on Google Cloud Dataproc, load data from a storage bucket, and implement metrics and a factorization algorithm in Spark.

upload the product rater file to a storage bucket from the master node, using browser or ssh, and manage authentication and file size considerations for efficient cloud upload.

Copy the product rater file to GCP, move it into Hadoop storage via Haloo FS, and load it into Spark from Hadoop using the get utility to avoid reloading.

Load data with Spark, transform it into a data frame, and split the data into training and testing sets with an 80/20 ratio for machine learning.

Train an ALS-based model by configuring initial values and input columns, then run iterations to fit the model. Adjust max iterations for accuracy and review SPARC documentation for details.

Train a model in a distributed system, split data into training and test sets, generate predictions, and evaluate them with root mean square error on the test data.

Explore basics of machine learning with collaborative filtering, reducing error toward convergence, and understanding slope. Implement in Spahr using the product rater file and assess via root mean square error.