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via Udemy |
Go to Course: https://www.udemy.com/course/big-data-and-machine-learning-part-1-extract-data-from-pdf/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on Big Data and Machine Learning geared toward beginners: --- **Course Review and Recommendation: Introduction to Big Data and Machine Learning with Python** If you are an absolute beginner with no prior programming experience and are eager to delve into the world of Big Data and Machine Learning, this Coursera course is an excellent starting point. Designed to guide novices step-by-step, the course emphasizes hands-on learning through real-world data examples, making complex concepts accessible and engaging. **What You Will Learn:** This course focuses on fundamental processes such as data collection, extraction, and visualization, which are crucial in Big Data and Machine Learning workflows. Beginning with the basics of Python programming, it covers installing Python and relevant IDEs like Anaconda and VS Code. The course then guides you through extracting data from PDF documents using tools like Apache Tika OCR and converting this data into text and tabular formats. A significant strength of this course is its practical approach. You will work on actual datasets, transforming PDF files into structured data, and visualizing the results using Python libraries such as Pandas, Seaborn, and Matplotlib. The course also introduces you to cloud-based platforms like Kaggle for data analysis and visualization, as well as version control with GitHub—essential tools in data science. **Course Content Breakdown:** - Installing and setting up Python and IDEs - Extracting data from PDFs using OCR and Apache Tika - Regular expressions for pattern matching - Managing data with arrays and functions in Python - Creating and manipulating Pandas DataFrames - Uploading data to Kaggle for analysis and visualization - Introducing GitHub for code management - Practical exercises with real datasets, including extracting data from PDF drawings **Pros:** - Beginner-friendly and highly practical - Clear, step-by-step instructions with real data examples - Covers essential tools and workflows used in data science - Focus on problem-solving and troubleshooting common installation issues - Incorporates popular platforms like Kaggle and GitHub, which are valuable in professional data science work **Cons:** - The course is part of a series, so it may be necessary to follow up with subsequent parts for a fuller understanding - Assumes some basic familiarity with computers, but no programming background is required **Who Should Enroll:** This course is highly recommended for anyone curious about Big Data and Machine Learning but lacking prior experience. It provides a solid foundation in data extraction, processing, and visualization, making it an ideal entry point into the data science field. **Final Verdict:** Overall, this course offers a very accessible and practical introduction to Big Data and Machine Learning, focusing on essential skills that will serve as building blocks for more advanced studies. Its emphasis on real data and step-by-step guidance ensures that learners can confidently start their data science journey without feeling overwhelmed. **Rating: 4.8/5** --- **Recommendation:** If you are starting from scratch and want a hands-on, beginner-friendly course that covers the basics of data extraction, visualization, and the tools used in Big Data and Machine Learning, this Coursera course is highly recommended. It will equip you with practical skills and confidence to explore further in the exciting realm of data science.
This course is intended to be an initiation to learn #BigData and #MachineLearning with #Python programming for absolute beginners that have no background in programming. In this course, we will step by step, using the example of real data, we will go through the main processes related to the topic "Big data and machine learning". Since the material turned out to be voluminous, I divided the course into five parts. ⇉ This part - the first part is devoted to the collection and extraction of data from documents. In this course, you will learn how to extract data from PDF documents, drawings and any other documents in PDF format. ⇉ We will work on real data. We will have two sets of data consisting of PDF files that we will transform to the text and to tabular form. We will visualize the received data on the Kaggle platform using python libraries, which will help us to depict our received data in a graphical format. ⇉ During the training process, we will install Python and such libraries as Pandas, seaborn, matplotlib and others. We will upload the received data to the Kaggle platform and here using the "Jupiter Notebook" we will visualize our data and at the end, we will upload our data to the GitHub platform.⚐ Topics covered in this course: Lecture 2. Python. Choosing python IDE. Anaconda. Install Python.How to convert a PDF to text?Python or Anaconda?What is the best Python IDE for beginners?How do I install VS Code?How do I install Python?How to run Python in VS Code?How does Python interpreter choose VS code?Lecture 3. 1st Dataset. PDF files. Tika OCR. Extracting content and metadata.How do I convert a PDF to TXT in Python?How can I iterate over files in a given directory?Install Apache Tika on Windows.How to split a string into a list?Remove blank strings from a list?Lecture 4. Regular Expression in Python. Pattern matching in Python.What is regular expression with example?How to match regular expression in Python?Debug a regular expression in Python?What is the regular expression for date format?How do you check if an array contains a regular expression?Create loop with regular expression.Lecture 5. Array und Function in Python. Add data to Array. Create function.How do you add a string to an array?How do you find the index of an element in a list?How can I extract the date from a string?How to declare and add items to an array in Python?How do you write a function in Python?Lecture 6. Pandas DataFrame. Two-dimensional size-mutable, tabular data structure.Install pandas on PythonHow do I create a pandas DataFrame?Reduce number of columns in a pandas DataFrameCombine column values into a list in a new columnHow to convert array into DataFrame in Python?How to change column names in pandas Dataframe?Save a Dataframe as CSV tableLecture 7. Kaggle. Jupiter Notebook. Create an account. Plotting with matplotlib and seaborn.Upload a file to kaggle kernelHow do you use kaggle dataset?Run Jupyter notebook using Kaggle kernelsConvert a CSV to dataframe in Python Jupyter NotebookHow to use the functions of Pandas Dataframe?Change the date format of a column in pandasHow do I convert a string to datetime Objects in Python?Calculate Difference Between Two Dates in Pandas DataframeHow do I delete a column in pandas DataFrame?Add columns in pandas DataFrame?How do you visualize a dataset?How do you plot a DataFrame in pandas?Lecture 8. 2nd Dataset. Task. Data from PDF. Getting data from PDF drawings.Independent Work TasksLearn to Code - on real data (16 PDF files to chart)A brief overview of the data in the taskLecture 9. 2nd Dataset. My solution.This is my solution.It may seem very simple and perhaps not the most effective.Lecture 10. GitHub. Desktop GitHub. Store and manage codeWhat is GitHub and how do you use it?What can I use GitHub for?How do I upload files to GitHub?Install GitHub DesktopHow to sync with a remote Git repository?Adding a repository from your local computer to GitHub⇛ This is a practical course where we will analyse the process of data extraction step-by-step. In this course, you will go through all the steps from installing python to data visualization on the Kaggle platform.When I got acquainted with the topic "Big Data and Machine Learning" myself, I often came across problems when installing software and errors while installing various libraries and tools for working with big data. ⇉ It took me a lot of time to find the right solutions and I would like to save this time for you. ⚐ To understand the topic, I had to look for a large number of questions for which I received non-targeted answers. In this course, you will find answers to basic questions that are related to the topic of Big Data and Machine Learning. Part 1: Extract Data from PDF.