Build Spark Machine Learning and Analytics (5 Projects)

Predict customer response to bank direct marketing campaigns using Apache Spark machine learning, with decision tree and logistic regression. Learn data preparation, analysis, and model deployment on a Spark cluster.

Explore Apache Spark as a high-performance general-purpose engine that distributes workloads across cluster nodes, supporting structured data processing, machine learning, graph processing, and streaming, with notebooks for development.

Learn to create a free Databricks account by visiting the community site, clicking sign up, selecting get started, entering your work email or Gmail, and confirming your registration.

Sign up for a free Databricks Community Edition account by visiting Databricks.com, selecting Get started for free, receiving credentials by email, and logging in.

Log in to the platform, open the cluster page, and create a cluster named sparklers. Watch the status change from pending to active as Supercluster comes up.

Explore predictive analytics with supervised and unsupervised learning, training models from feature-rich labeled data and discovering patterns in unlabeled data using Spark libraries.

Create a notebook, name it, and run runnable cells to execute code and see outputs like one plus one equals two, using magic commands such as %python and %shell.

Discover Spark dataframes: load data with schema, select columns, filter where price > 2, create temporary views, and query with Spark SQL while visualizing results in notebooks.

Explore random sampling and a 70/30 train-test split, then build models like logistic regression and decision tree using a vector assembler to combine numerical features and apply transforms and regularization.

Explore a project workflow in spark machine learning and analytics by uploading and importing datasets and building a customer response predictor in project explanation part 1.

Explore predicting customer response to a banking direct marketing campaign using decision tree and logistic regression, including loading semicolon-delimited CSV data, preparing features, and running models.

Learn to create or replace a temporary view from a data frame in Spark, name the view (for example bank_data), and run a select star query to explore the data.

Examine bank marketing dataset features: job type, education, marital status, default credit, housing and loans, contact type, call duration, campaign metrics, and socio-economic indicators, for building Spark machine learning models.

Explore exploratory data releases with graphical techniques to uncover structure, detect outliers, test assumptions, and build a parsimonious model to identify optimal factors for product subscription amid label imbalance.

Build a Spark logistic regression model by transforming categorical features with string indexer and vector assembler, train-test split, and evaluate to about 86% accuracy, then 91% with a decision tree.

Welcome back, please provide a different rating to the schools, and thank you for enrolling; I wish you all the best for your future.

Learn to predict online shopper purchase intention using logistic regression on the party's machine learning data platform, from setting up a smart cluster to data processing and model evaluation.

Explore Apache Spark as a high performance cluster engine that distributes workloads across nodes, supports Spark SQL, machine learning, graph processing, and streaming, with notebooks for hands-on predictive analytics.

Follow along to create a free Databricks account by navigating the sign-up flow, entering a work email, and confirming your registration before signing in.

Visit Databricks, sign up for the free Community Edition, receive credentials by email, and log in to start practicing on the Databricks platform at no cost.

Log in to the platform, navigate to cluster, create a named cluster, and watch its status move from pending to active as you provision a spark cluster.

Explore predictive analytics and machine learning, including supervised learning with features and labels to train a model for prediction, and unsupervised learning for clustering without labels.

Create and name notebooks, then run code in runnable cells to see outputs, using magic commands like %python and %shell for documentation.

Explore dataframes in Spark to load data with explicit schema, select columns, filter rows (price > 2), and create in-memory temporary views for interactive analytics and Spark SQL queries.

Learn key spark ml terms: random sampling and a 70/30 train-test split, vector assembler to unify features for models like logistic regression and decision trees, and regularization concepts.

Upload and import the source code into the spark environment, then open the online shopper data file. Build a spark machine learning model to predict whether a user will buy.

Load the online shopper intention dataset in a Spark environment, using a comma delimiter, then display the data frame and print its schema to verify columns and records.

Learn how e-commerce analytics use numerical and categorical features, with revenue as the class label, including bounce rate, page value, and special day effects from website logs and Google Analytics.

Describe data with count, mean, standard deviation, min, and max for columns; create a Scala template, execute code, and generate histograms to analyze revenue and traffic over time.

Explore an imbalanced dataset to predict revenue and product purchases, using SQL queries, bar graphs and scatterplots, and weekend trends to guide promotions.

Explore visitor types, including new, returning, and other visitors, along with browser and traffic analytics, regional distribution, and operating system usage through bar graphs to guide marketing and website optimization.

Explore revenue drivers by information duration and product related duration using group by, box plots, and scatter plots; analyze traffic types, regions, and visitor segments.

Implement a logistic regression model in a Spark pipeline to predict online shopper participation using feature extraction, indexing, and vector assembly, and achieve 91 percent accuracy on a 70/30 split.

Thank students for enrolling in the build spark machine learning and analytics (5 projects) course and wish them success in their future after completing the program.

Explore Apache Spark to analyze web server logs, process big data, and generate reports through data transformations, visualizations, and Spark SQL queries, with hands-on setup across platforms.

Explore how Spark distributes workloads across a cluster for high-performance computation with Spark SQL, machine learning, graph processing, and streaming. Use notebooks to develop with Spark and enable predictive analytics.

Master the basics of dataframes in Spark by loading data with a schema, selecting columns, filtering by price > 2, and creating in-memory temporary views for sql queries and visualizations.

Navigate to the Databricks community site, click sign up, complete the form with a work email or Gmail, confirm the email, and sign back in.

Register for a free Databricks community edition account, receive credentials via email, and log in to access the Databricks home page for hands-on practice.

Log in to the community site, open the cluster page, name the cluster, click create, and watch the cluster become active, provisioning a spark cluster.

This lecture introduces the basics of the notebook in the Spark machine learning course, showing how to create notebooks, write and run code cells, and use magic commands.

Master the essential first steps to lock down your community by uploading profiles and documents. Navigate the download folder to ensure file and poll uploads succeed.

Learn how to perform server log analysis with Spark, using real-world text data to monitor servers, gain business intelligence, and build recommendations and fraud detection solutions.

Load and process a large dataset for a Spark machine learning project, addressing data formatting, headers, and upload performance. Document steps, monitor progress, and interpret results across the environment.

Learn how to design and apply regular expressions to extract user names, request status, and timestamps from data. Handle missing values and extend status rules.

Learn to compute minimum, maximum, and average values from a data column, use regular expressions to parse status, and group by status and order by status to reveal top results.

Parse server logs with regular expressions and split functions to extract dates and columns, then aggregate daily requests and 404 errors to generate top error reports.

Publish your project on the web and share the link in your resume to impress recruiters, making your work accessible to anyone with an internet connection.

Celebrate completing the build spark machine learning and analytics course. Provide a different rating to the schools, and I wish you all the best for your future.

Explore predictive analytics with Apache Spark, set up a Spark cluster, and build machine learning models to generate predictions from big data through hands-on, step-by-step labs.

Explore Apache Spark, a high-performance cluster engine that distributes workloads across nodes and supports structured data, machine learning, and streaming. Use notebooks to develop Spark applications and perform predictive analytics.

Discover how to create a free databricks account by navigating the sign-up flow, entering a work email or Gmail, and confirming the account before signing in.

Go to Databricks.com, click get started for free to access the community edition, receive email credentials, and log in to practice on the Databricks platform for free.

Learn how to provision a spark cluster by logging in, creating a cluster named sparklers, and confirming it becomes active.

Explore how supervised and unsupervised learning build predictive models from features and labels, train with labeled data, and apply to new data or discover clusters using Spark libraries.

Create and name a notebook, write runnable cells, and use notebook magic commands to run code, document with text and equations, and view outputs.

Load data into dataframes, select specific columns, filter rows where price > 2, and create a temporary view to run Spark SQL and visualize results interactively.

Explore the basics of dataframes in Spark by loading data with an explicit schema and defining a case class for six columns. Apply Spark SQL for aggregation and basic statistics.

Explore classification with structured and unstructured data, predicting on-time versus late labels using logistic regression and features such as starting destination, arrival, and destination.

Build a classification model with spark mllib by loading a railway delay dataset, using vector assembler to combine features, and training a logistic regression model with a 70/30 train-test split.

Explore regression models as a predictive modeling technique that examines the relation between dependent and independent variables to forecast outcomes, with a focus on linear regression.

Build a regression model with spark ml to predict railway arrival delays using day of week, origin, destination, and departure delay, with a 70/30 split and linear regression.

Explain terms used in spark machine learning models, including train-test splits (70/30), vector assembler to combine features, and transform for scaling. Discuss regularization parameters and regression techniques to handle overfitting.

Thank you for enrolling in the course and for your participation. Wish you all the best for your future, and please provide a different rating for the schools.