RPA & Intelligent Automation Using Python

Explore the course structure for RPA and intelligent automation using Python, including RPA concepts, Python, and RPA using Python, with resources to download code, data sets, and pandas.

Learn how RPA automates mundane, rule-based tasks by mimicking human actions with software, standardizing processes to reduce errors and boost productivity in invoicing, reporting, and vendor payments.

Explore how intelligent automation builds on RPA by applying AI, OCR, and NLP to automate complex tasks, handle unstructured data, and improve ROI with humanlike chatbots.

Learn how task automation and workflow automation optimize an end-to-end vendor payment process by extracting data from purchase orders, invoices, and goods receipt notes and feeding SAP for payments.

Compare task vs workflow automation to optimize headcount and costs. Stress granular process documentation, including field names, application entries, and process mining tools, for successful automation deployments.

Explore an RPA case study in an organization that automates extracting material code and net amount from scanned invoices, updating Excel and SAP, delivering end-to-end automation that frees employee time.

Explore Python in Google Colab, sign in with your Google account, connect to Google Compute Engine, and practice fundamentals like hello world, strings, and comments while debugging with explain error.

Explore Python variables, naming rules, and case sensitivity. Use the type function to inspect integers, floats, and booleans, and practice casting between types to drive if statements, filters, and loops.

Explore Python operators: comparison, relational, logical, membership, and identity. Learn how these operators evaluate expressions, control flow with conditionals, and test membership and object identity.

Explore Python conditions with if, elif, and else, mastering indentation, boolean operators, and nested checks, while avoiding common mistakes and leveraging one-line alternatives for clear code.

Explore Python loops, comparing while and for constructs, and learn how break and continue control iteration to process elements or end loops early.

Understand functions in Python, including user defined and pre-defined library functions like NumPy and pandas, to promote code reuse and clean organization for machine learning tasks.

Master arrays as a fundamental data structure: lists, tuples, sets, dictionaries. Compare ordering, mutability, and duplicates, and learn creation with brackets for one and two-dimensional arrays, important for machine learning.

Learn how to work with Python lists, including creating single-dimensional arrays, indexing from zero with negative indices, and using append, remove, delete, copy, and clear to manage items.

Explore Python tuples as immutable, ordered collections that allow duplicates, indexing, slicing, and membership checks; convert to a list to modify, then back to a tuple, and join with +.

Explore Python sets, which are unordered and unindexed, disallow duplicates, and are created with curly brackets; learn membership tests, add and update operations, and set unions.

Explore how to create and manipulate dictionaries in python using curly brackets, enforce unique keys, print dictionaries, access and update values, and note dictionaries are not ordered.

Master NumPy basics by importing numpy as np, creating one-, two-, and three-dimensional arrays, and using indexing, slicing, and copy versus view for memory efficiency.

Explore numpy shape, reshape, and flattening for data analysis and modeling. See 2x5 sales matrix, reshape a 10-element array to 5x2, and flatten with -1 for export or model input.

Explore three NumPy array iteration methods: row-wise, nested loops, and flat iteration, to analyze sales data and apply conditional logic efficiently.

Learn to join NumPy arrays across different axes to align data for analysis, using hstack, vstack, and depth stacking for one- and two-dimensional arrays.

Explore splitting arrays with NumPy for analytics and batch processing using np.array_split to make equal or nearly equal subarrays. Learn how np.split raises an error when divisibility fails, unlike np.array_split.

Explore numpy's np.where to locate indices by conditions and sort arrays with np.sort, organizing numerical and categorical data like city names.

Learn to manipulate structured data in Python using pandas and its data frame and series structures, built on NumPy, for filtering, grouping, and time series analysis.

Explore basic data analysis with pandas, inspect data types, view samples with head and tail, use df.describe to study mean, std, and percentiles, and learn histograms.

Explore sorting, slicing, and grouping data in pandas by age, then selecting columns, using iloc, transposing, copying, updating with loc, and calculating means of numeric columns.

Visualize data with matplotlib by creating bar charts, line charts, scatterplots, box plots, and 3D plots from a pandas data frame, exploring hours studied versus marks.

Learn to use regular expressions in python for process automation, performing search, find all, compile, split, and replace operations to extract patterns like employee numbers, invoice numbers, and dates.

Create a new Excel workbook with openpyxl, build row data from aligned name, address, and organization lists using zip, and append rows to the active sheet before saving.

Split an Excel file into multiple city-based worksheets by loading with Openpyxl, extracting headers, using NumPy to create city sheets with matching rows, and saving the workbook.

Generate a random Excel dataset with name, college, age, and grade; use Openpyxl and Pandas to create a pivot table with pd.pivot_table in Python.

Learn to perform vlookup in Excel programmatically with Python using openpyxl and pandas, creating a random data set and applying vertical lookup to replicate salary across columns.

Automate multiple tasks by merging two files with pandas on invoice numbers, create a pivot table and graphs, and save the results as a PDF in Colab.

Create visuals from Excel data using Python, then export them to a PowerPoint deck. Learn Pandas data handling, Matplotlib charts, and automating slides with python-pptx.

Learn to convert Excel data into a polished PDF report using Python libraries like pandas, matplotlib, and ReportLab, including a dual-axis chart of units sold and profits and executive-style summaries.

Create a polished, multi-sheet Excel report from three product sheets using Python, Pandas, and Openpyxl, including profit calculations, aggregations, and conditional formatting for readability.

extract tables from multiple PDFs, normalize headers, clean numeric data, combine into a single Excel file, and apply borders and table formatting for readability.

Learn to extract specific information from pdf documents, such as invoice date and invoice number, using PyPDF and regex in a Colab workflow with Google Drive integration.

Extract invoice numbers from images using Python with Tesseract OCR and Pytesseract, applying RGB conversion, grayscale, sharpening, and binarization, then regex patterns to identify 6–12 character IDs for end-to-end automation.

Learn to extract information from websites using web scraping with beautiful soup, requests, and a regular expression, save SeaportAI.txt in automation folder on Google Drive, and run in Google Colab.

Apply rpa and intelligent automation skills to scrape tables from websites using urllib, beautiful soup, numpy, and pandas, extracting rank, country, and rate, and exporting as csv or xls.