10 Google Drive Tips That Will Save You Hours Every Week

If you're simply using Google Drive as a repository for files, you're losing out on an enormous time-saving boon. From automation to advanced search tricks, Google Drive has serious productivity features at its disposal, many of which are hiding in plain sight.

In this post, you'll discover 10 useful Google Drive tips that will make you work faster, smarter, and better organized saving you hours a week.

Tip 1: Use Keyboard Shortcuts to Speed Up Navigation

Why click around when you can execute actions instantly? Google Drive comes packed with keyboard shortcuts that drastically reduce mouse usage.

Top shortcuts:

• Alt + C then T – Create a new Google Doc

• Alt + C then S – Create a new Google Sheet

• Alt + C then P – Create a new Google Slides

• Alt + C then O – Create a new Google Forms

• Alt + C then F – Create a new folder

Pro Tip: Hit Ctrl + / or Cmd + / (Mac) inside Drive to view the full list.

Google Drive Keyboard Shortcuts Panel

Tip 2: Color-Code and Organize Your Folders

Keep your Drive from becoming digital chaos. Assigning colors to folders helps you spot the right one at a glance.

How to Color-Code Folders in Google Drive

1. Right-click on the folder you want to color.

2. Hover over “Organize” in the menu.

3. Click on the color you like, the folder will instantly change to that color!

Organizing Google Drive Folders with Color Labels

How to Add Emojis to Folder Names

1. Right-click the folder you want to rename.

2. Click “Rename”.

3. Open an emoji keyboard:

   • On Windows: Press Windows Key + . (period)

   • On Mac: Press Control + Command + Space

4. Pick an emoji and type your folder name. Example: 📚 School, 📁 Projects, 📝 Assignments

5. Click “OK” or press Enter to save.

Renaming and Personalizing Folders in Google Drive with Emojis

Tip 3: Master Advanced Search Operators

Stop scrolling through dozens of files. Use search operators to filter results in seconds.

Examples:

• type:pdf – Find only PDFs

• owner:me – View only your files

• before:2023-01-01 – Files created before 2023

  
  

PDF File Search Results in Google Drive

Tip 4: Use Version History Instead of Saving Duplicates

1. Open your Google Doc/Sheet/Slide that you want to review.

2. Click on "File" in the top-left menu.

3. Hover over “Version history” in the dropdown.

4. Click “See version history”.

Accessing the "Version History" option from the "File" menu in Google Docs

5. A sidebar will open on the right, showing all saved versions with timestamps (and names, if any).

6. Click on any version to:

   • Preview what the doc looked like at that time

   • Restore that version if needed (there’s a "Restore this version" button at the top)

Viewing detailed version history with timestamps and editor names in Google Docs

Tip 5: Download Files in Bulk with One Click

1. Hold Shift or Ctrl (Cmd on Mac) to select multiple files

2. Right-click → Click “Download”

3. Files will be zipped and start downloading

Multiple file selection and zipping in Google Drive.

Tip 6: Use Google Drive Add-Ons & Extensions

Power up Drive with third-party tools. Head to the Google Workspace Marketplace to explore add-ons.

Favorites:

• DocuSign: Sign documents directly

• Lucidchart: Create diagrams in Docs/Sheets

• HelloSign, Trello, Grammarly, and more

 Google Workspace Marketplace showing business productivity add-ons

Tip 7: Convert PDFs and Images into Editable Docs

Using Google’s built-in OCR, you can transform scanned PDFs or images into editable Docs.

Steps:

1. Upload PDF/image to Drive

2. Right-click → “Open with” → Google Docs

PDF opened as editable Doc

Tip 8: Comment and Tag Collaborators with @Mentions

Why it helps: Get quick feedback and notify teammates instantly.

How to do it:

1. Highlight text in a Doc, Sheet, or Slide

2. Right-click → Click “Comment”

3. Type @ followed by their name/email (like @John Smith)

4. They’ll get an email alert, and can reply or edit directly

Commenting and tagging a collaborator in Google Docs.

Tip 9: Share Files with Anyone Using a Link

1. Right-click the file and click “Share”

2. Click “Copy Link”

3. Change the access to “Anyone with the link”

4. Share that link via email, chat, or any platform

Sharing Files with Anyone Using a Link

Tip 10: Use Voice Typing in Google Docs

Dictate your documents instead of typing to save time.​

How to:

1. Open a Google Doc.​

2. Click on "Tools" and select "Voice typing."​

3. Click the microphone icon and start speaking.

Voice Typing Tool in Google Docs

Conclusion

Google Drive isn’t just your average cloud storage; it’s a dynamic productivity powerhouse just waiting for you to tap into its full potential. With handy keyboard shortcuts, color-coded folders, voice typing, and advanced sharing options, these tips can really help you streamline your daily tasks. Even if you only adopt a few of these features, you’ll find yourself saving time and keeping your files organized and easy to access.

Whether you’re a student, a remote worker, or simply someone who wants to stay on top of their digital game, mastering these Google Drive tricks will give you a significant edge in efficiency.

FAQs

1. Are Google Drive keyboard shortcuts available on mobile devices?

No, keyboard shortcuts are only available when using Google Drive on a desktop or laptop browser.

2. Can I recover accidentally deleted files from Google Drive?

Yes! Deleted files go to the Trash and stay there for 30 days. You can restore them anytime during that period by going to the Trash folder, right-clicking the file, and selecting Restore.

3. Do I need internet access to use Google Drive features?

For most features, yes. However, you can enable Offline Mode to access and edit files without internet. Go to Settings > Offline > Enable Offline Mode.

4. What file types can Google Drive preview and convert?

Google Drive can preview PDFs, images, videos, audio, MS Office files, and more. You can also convert PDFs, images, and Word files into Google Docs using the “Open with” → Google Docs option.

5. How do I stop people from downloading or copying shared files?

While sharing, click Settings in the share window and uncheck “Viewers and commenters can see the option to download, print, and copy”.

Clean Up Gmail: Secrets to Finding and Removing Space Hogs

"Transform Your Gmail: From Overloaded to Optimized Storage!"

Is your Gmail running out of space? Ever stared at that dreaded “Storage almost full” alert and wondered, “Where did all my space go?” You’re not alone! With Google’s free storage—shared across Gmail, Google Drive, and Google Photos—it can feel like your space is vanishing into thin air. Over time, emails with large attachments, years of accumulated messages, and forgotten files clog up your account, leaving you scrambling to free up storage. But don’t worry—there’s a solution! In this guide, we’ll uncover the sneaky storage hogs hiding in plain sight and show you simple yet effective ways to clean up your Gmail. Whether it’s deleting old emails, removing unnecessary attachments, or managing shared storage across Google services, you’ll learn the secrets to reclaiming space and keeping your inbox running smoothly. Let’s dive in!

Hidden Gmail Storage Hogs You Didn’t Know About (And How to Remove Them)

Is your Gmail storage running out, and you’re not sure why? Sometimes, the things filling up your space are hidden and hard to notice. Here are some sneaky culprits that take up storage and simple tips to get rid of them.

1. Promotional Emails with Images or Attachments

Promotional emails, like newsletters or ads, often include big images, PDFs, or other files. These emails arrive regularly and pile up without you realizing it. Over time, they take up a lot of space in your inbox.

How to Remove Them:

• Search for promotional emails by typing unsubscribe in Gmail’s search bar. This will show emails with an “Unsubscribe” button, which are usually ads.

• Select these emails and delete them in bulk.

"Effortless Email Cleanup: Unsubscribe and Declutter Your Inbox in Seconds!"

• To stop future clutter, click the “Unsubscribe” button in these emails.

"Say goodbye to unwanted emails—unsubscribe effortlessly."

2. Files Attached to Emails (Saved in Google Drive)

When you send or receive files through Gmail, they are often saved in Google Drive. Since Gmail and Google Drive share storage space, large files like videos or presentations can quickly fill up your account.

How to Find Large Files:

• In Gmail: Type size:5MB has:attachment in the search bar to find emails with big attachments.

"Find and Remove Large Emails: Gmail Search Filters at Work!"

• In Google Drive:

  • Open Google Drive.

  • Click “Storage” on the left side.

  • Sort files by size to see the biggest ones.

• Delete files you don’t need or move them to an external hard drive.

"Identify Large Files in Google Drive: Reclaim Your Gmail Storage!"

3. Archived Emails

Archived emails are hidden from your inbox but still count toward your storage limit. These might be old conversations you’ve forgotten about but never deleted.

How to Manage Archived Emails:

• Search for old emails using older_than:1y (emails older than one year).

• Review these emails and delete the ones you don’t need anymore.

"Quick Tip: Use Gmail Search Filters like 'older_than:1y' to spot and clear old emails!"

4. Spam and Trash Folders

Did you know that emails sitting in your Spam and Trash folders also use up storage? Even though Gmail deletes them after 30 days, they can still take up space if left unchecked.

How to Clear Spam and Trash:

• Go to the Spam and Trash folders in Gmail.

• Select all emails and delete them permanently.

• Make it a habit to check these folders regularly.

"Declutter Your Gmail: Permanently Delete Spam Emails to Free Up Space!"

Bonus: Tools to Simplify the Process

Managing Gmail storage can be easier with the right tools. Here are some features and tools that can help:

1. Gmail’s “Storage Management” Feature

Gmail has a built-in tool to help you manage storage. It shows how much space you’re using and lets you clean up large emails and files.

How to Use It:

• Click on your account picture in Gmail.

• Look for the storage bar and click on it.

• You can delete big emails and files from there.

2. Third-Party Tools

If you need more help, here are some other tools:

• Find Big Mail: This tool finds large emails in your inbox and helps you delete them.

• Google Takeout: Use this to download your emails and files before deleting them from Gmail.

Tips for Using These Tools

1. Use Both Built-In and Third-Party Tools: Combine Gmail’s tool with others like Find Big Mail for a better cleanup.

2. Backup Before Deleting: Use Google Takeout to save important emails before deleting them.

These tools make it easy to keep your Gmail account organized and free up space!

Conclusion

By regularly decluttering your Gmail inbox, you can stay ahead of storage issues and enjoy uninterrupted service. Implementing tips such as deleting old emails, managing large attachments, and utilizing archiving ensures your inbox remains organized and efficient135. With these strategies, reclaiming space becomes simple, empowering you to maintain a seamless email experience without the frustration of hitting storage limits26. Take charge of your digital space today and keep your Gmail running smoothly!

Using Scikit-learn to Build a Model from Non-Tabular Data: Audio, Text, and Images

In this blog, we’ll explore how to use Scikit-learn for building machine learning models with non-tabular data such as audio, text, and images. These data types require special preprocessing techniques, which we will cover in this post. By the end of this guide, you will have a deeper understanding of how to work with these non-tabular data formats using Scikit-learn and related libraries.

 Step 1: Setting Up the Environment

Before getting into each type of non-tabular data, let's ensure our environment is set up with the necessary libraries.

We’ll need:

• Scikit-learn: For building and evaluating machine learning models.

• NumPy: For numerical operations.

• Pandas: For handling any data manipulation (though minimal here).

• Matplotlib/Seaborn: For visualization.

• Librosa: For audio data handling.

• nltk: For text data preprocessing (Natural Language Toolkit).

• OpenCV or Pillow: For image processing.

  
  

You can install these libraries via pip:

Now, let’s import all the libraries into our project:

Imported required libraries

Step 2: Audio Data — Building a Model with Scikit-learn

Audio Data Overview

Audio data is a type of non-tabular data that is usually represented as waveforms, which show how sound (or air pressure) changes over time. These waveforms are essentially long sequences of numbers, and while they carry rich information, they are not directly usable by machine learning models. That’s why we need to preprocess the audio and extract useful features that represent important aspects of the sound, like pitch, frequency, and tone. This process is called feature extraction.

Two common methods for this are MFCC (Mel Frequency Cepstral Coefficients) and spectrograms. MFCCs are widely used in speech and music applications because they mimic how the human ear hears sound, capturing the tone and texture.

Spectrograms, on the other hand, are like colorful heatmaps that show which frequencies are present at each moment in time and how strong they are. These features help turn raw audio into meaningful patterns that models can learn from. Once extracted, these features can be used to train traditional machine learning models (like Random Forest or SVM) for tasks such as speech recognition, audio classification, and emotion detection.

 Loading Audio Data with Librosa

For the purpose of this demonstration, we'll use a freely available sample audio file that comes with Librosa.

In the code above:

• y represents the audio waveform (the raw audio signal).

• sr is the sampling rate (how frequently the audio was recorded).

Extracting Audio Features with MFCC

Now that we have the audio loaded, we’ll extract MFCC features, which are a popular feature set for speech recognition and other audio-based machine learning tasks.

Extract MFCC features from the audio signal

Plot the MFCCs

In this code:

librosa.feature.mfcc() extracts 13 MFCC coefficients from the audio signal.

We use librosa.display.specshow() to display the MFCC features as a heatmap over time.

These MFCCs will be the features that we feed into a machine learning model, which can learn patterns from the audio.

Preparing the Data for ML Models

Before we can train a model, we need to prepare the data. Here, we’ll flatten the MFCCs into a 1D array and normalize the values. This step is necessary because most machine learning models expect a 2D input (samples x features).

Flatten the MFCCs to create a feature vector for each audio sample

Reshape the data (e.g., for one audio sample)

Sample target labels (for simplicity, let's use dummy labels for now)

In the above code:

np.mean() computes the mean of each MFCC coefficient over time.

StandardScaler() normalizes the features to have a mean of 0 and a standard deviation of 1, which is often helpful for machine learning models.

Training an Audio Classification Model

Now that we have the audio features (MFCCs), let's train a machine learning model. We will use Random Forest for this example, but you can easily swap it with other models like SVM or KNN.

Train a Random Forest classifier

Predict the class of the same sample (for simplicity) and Evaluate the model

This code:

• Initializes and trains a Random Forest classifier.

• We then predict the class of the same sample (which is a simplification—usually, we would split the data into training and test sets).

• Finally, the model’s accuracy is printed.

Step 3: Image Data — Building a Model with Scikit-learn

Now that we've covered audio, let’s move to image data. We'll convert images into feature vectors that can be fed into machine learning models.

Images are a type of non-tabular data made up of pixels. Each pixel represents color and brightness values typically stored as numbers. But machine learning models can't directly work with images in their original format (like .jpg or .png). So we need to convert these images into numerical arrays that models can understand.

To do this, we’ll use image processing libraries like Pillow or OpenCV. These tools help us:

• Open and display images

• Convert them to grayscale (if needed)

• Resize them to a consistent shape

• Normalize the pixel values (scale them between 0 and 1)

• Flatten them into a 1D feature vector

 Loading and Preprocessing Image Data

We'll use Pillow or OpenCV to handle images. Let’s load an image and flatten it into a 1D vector:

This 1D vector becomes the input to our machine learning model just like rows of a table. For example, a 28x28 grayscale image will be turned into a 784-element vector (because 28×28 = 784). This format works well with traditional models like Random Forest, Logistic Regression, or SVM.

Load an image

Convert the image to a numpy array and flatten it and Normalize the pixel values (scale between 0 and 1)

Training an Image Classifier

Now we’ll train a machine learning model (Random Forest, for instance) on the image data:

Predict the class of the same image and Evaluate the model

 Step 4: Text Data — Building a Model with Scikit-learn

For text, we need to vectorize the text into numeric format, and TF-IDF (Term Frequency-Inverse Document Frequency) is a great method for this task.

Text Data Overview — Why Vectorization is Needed

Text is one of the most common and powerful sources of data in the real world — think of product reviews, tweets, news articles, or chatbot messages. But machine learning models can’t directly understand raw text like we do. They need numbers, not words.

So, before we can train a model on text data, we must convert it into numerical format, a process known as vectorization. One of the most effective and widely used techniques for this is TF-IDF, which stands for Term Frequency–Inverse Document Frequency.

 What is TF-IDF?

• Term Frequency (TF) tells us how often a word appears in a document. Words that appear more often are generally more important.

• Inverse Document Frequency (IDF) tells us how common or rare a word is across all documents. Words that appear in almost every document (like “the”, “is”, or “and”) are less useful for distinguishing between texts, so they get lower weight.

TF-IDF combines both ideas. It gives higher importance to words that are frequent in a document but rare across other documents which makes them more useful for understanding the content or topic.

This method transforms each document (a sentence, review, etc.) into a vector of numbers. These vectors can then be used as input features for traditional machine learning models like Logistic Regression, Naive Bayes, or SVM.

Text Preprocessing with TF-IDF

Sample text data (let's classify movie reviews)

Labels (1 = positive, 0 = negative)

Training a Text Classification Model

Train a logistic regression model

Predict and evaluate

Conclusion:

In this blog, we covered how to handle audio, text, and image data with Scikit-learn. Here’s a quick recap:

Data Type	Feature Extraction	Scikit-learn Model
Audio	MFCC	Random Forest, SVM
Image	Pixel values	Random Forest, SVM
Text	TF-IDF	Logistic Regression, Naive Bayes

Each data type requires different preprocessing steps, but Scikit-learn can handle them once they are transformed into numerical features.

Feel free to experiment with these steps using your own data, and happy learning!