Complete PySpark & Google Colab Primer For Data Science

Mount your Google Drive in Colab to access datasets from your drive. Authorize the drive and confirm it is mounted at /content/drive for data loading.

Open a Jupyter notebook in Google Colab by right-clicking the ipynb file in Google Drive and selecting Google Colab, then import pandas as pd and access files in Drive.

Learn to access data stored in Google Drive from Colab, import pandas as pd, and read csv files via a drive path, then inspect cancer_tumor.csv.

Learn to read CSV data from a local drive in Google Colab by uploading files, using drag-and-drop, and reading with pandas read_csv and df.head.

Learn to set up PySpark in Google Colab, initialize a Spark session, and create a dataframe to verify installation.

Troubleshoot java version conflicts in Google Colab by selecting java 8 or compatible versions, set java home, and verify spark with pyspark, spark 3.0.1 and hadoop 2.7.

Create a spark session in a local environment, then read a csv with spark, enabling infer schema and header true, and inspect the schema and top rows to explore data.

Explore basic data exploration in PySpark on Google Colab by reading CSVs, creating a Spark session, and using describe, show, select, and filter to summarize and inspect data.

Read a csv with spark, group fires by land cover to count occurrences, then iterate over country, year, type, and El Nino attributes to compare counts and summarize results.

Standardize predictors by dropping non-predictor columns, selecting chemical variables, and applying PySpark's vector assembler and standard scaler before modeling.

Define a user defined function in PySpark to categorize ages into bins, apply it to a new age column, and prepare data by dropping nonessential columns in Google Colab.

Learn how correlation quantifies the relationship between two quantitative variables via scatter plots, with positive, negative, and no correlation, and note that correlation does not imply causation.

Implement a practical correlation analysis in PySpark by preparing data, selecting numeric features, assembling vectors, computing a Pearson correlation matrix, and visualizing results.

Explore the theory of linear regression, including simple and multiple regression, ordinary least squares, intercept and slope, residuals, predictions, R-squared, and p-values for significance.

Implement an ordinary least squares linear regression in PySpark on the Boston medv dataset, build predictors with vector assembler, and evaluate a 70/30 train-test split using MSC and R Square.

Apply elastic net regression to reduce overfitting by optimizing the bias-variance trade-off with regularization in a pyspark ml pipeline on the Boston csv data to predict medv.

Implement a logistic regression model in PySpark to classify malignant versus benign tumors using a vector assembler and binary classification evaluation.

Explore accuracy assessment in machine learning by evaluating training and test data, preventing overfitting, and using k-fold cross validation and leave out cross validation to gauge generalizability.

Learn to handle mixed datasets in pyspark by identifying string and numeric columns, applying string indexer and one-hot encoding to categoricals, and assembling features with vector assembler.

Learn the basic theory of machine learning, where algorithms learn from data to classify and predict, and compare unsupervised versus supervised approaches using remote sensing imagery as an example.

Explore random forests and gradient boosting as ensemble methods that combine weak decision-tree learners into strong predictors, and learn to assess predictor impact and implement these models in R.

Implement a multi-class random forest model in PySpark to classify the type, assemble predictors with a vector assembler, and use a 70/30 train-test split to fit and predict.

Evaluate the random forest model's accuracy on the test data with a multi-class evaluator, achieving 75% accuracy in predicting the type labels.

Explore building a random forest regression model in pyspark using the boston dataset to predict medv, from selecting predictors to 70/30 train-test split and RMSE evaluation.

Discover how spark ml pipelines sequence stages as string indexer and vector assembler to streamline preprocessing and training, grid or random search for regression and classification models in pyspark.

Build a random forest regression model with PySpark pipelines, using a vector assembler and parameter grid, then evaluate with regression evaluator via ten-fold cross-validation on a 70/30 Boston data split.

Explore unsupervised classification with k-means, selecting k clusters, initializing centers, assigning points by Euclidean distance, and iterating to minimize within-cluster variation while maximizing between-cluster separation.

Introduces k-means clustering in pyspark, reads the glass class dataset, selects predictor features, runs with 10 clusters, and evaluates with a silhouette score of 0.6 indicating moderate performance.

Explore the evolution of artificial intelligence, from Deep Blue to AlphaGo, and distinguish AI, machine learning, and deep learning while examining real-world applications in cancer detection, finance, and wildlife monitoring.

Explore the biology-inspired foundations of artificial neural networks, detailing neurons, weights, activation functions, and the shift from perceptrons to multilayer and deep neural networks, trained by backpropagation.

Set up a neural network in pySpark with a multi-layer perceptron classifier, nine predictors, seven classes, two hidden layers of five nodes, using a vector assembler and 70/30 train-test split.

Explore a binary malignant versus benign tumor classifier with an artificial neural network in PySpark, using 30 predictors, a 12-node hidden layer, and a 70/30 train-test split, achieving 91% accuracy.

Apply a spark-based neural network to mixed data using a pipeline with string indexers and vector assembler. Train, validate, and test a multilayer perceptron classifier and evaluate with multiclass metrics.

Explore activation functions and their role in neural networks, including sigmoid, tanh, relu, and softmax, for non-linear modeling and classification tasks.

Identify data types in statistics: categorical, numerical, and ordinal, with examples such as eye color, political affiliations, and hotel ratings. Distinguish continuous and discrete data for tailored analyses.