動画数：88件
- Overview
- Getting started on the Book Review API
- Set up Airtable as our database & connect to it with Python
- Implement code to add reviews and view all reviews
- Adding a POST request to our API
- Trying out our new endpoints (using documentation & requests library of Python)
- Commit code to Github & deploy live to Render.com
- Video overview
- What we're building
- How to get setup with Github template code
- Taking a look at the Flask, Flasgger Python3 code
- Testing some API requests (GET) locally
- Building another GET request endpoint (with multiple parameters)
- Using ChatGPT to help us build another endpoint
- Deploying our API to a live public URL endpoint (using render.com)
- Video overview & topics covered
- Basic regex syntax (building up an intuition)
- Character Sets Overview ([A-Za-z0-9])
- Quantifiers Guide (*, +, ?, {3,5})
- Guided Exercise: Find all words that don't use vowels
Linguistically speaking, [y] can be a vowel, especially in words like "crypt". Pedantry of course, since it could just be added into the regex if needed. 🤓
- Helpful cheat sheet to remember regex syntax in the real-world
- Matching words/patterns of a specific length ({3,5})
- OR operator overview
- Guided Exercise: Match valid sentences (starts with capital letter, ends with period)
- Character classes overview (\w, \b, \d, \s)
- Escaping Characters
- Practice Exercise #1: Write a regular expression to match meme text format
- Practice Exercise #2: Write a regular expression to match a specific date format
Might not really be up to regex to do data validation. There are better tools for that. 🧰In fact, integrating these into data workflows would be a good follow-up video for the future. ▶
- Groups overview
You could definitely get everything if you add an extra parenthesis around the thing you want to get in this case (([a-z][A-Z])+[a-z]?)@(\w+\.\w+)
- Lookahead & Lookbehind Assertions
- Practice Exercise #3: Detect if same word pops up multiple times in a sentence
- Practice Exercise #4: Password matching with rules
- Some final recommendations! (additional practice, chatgpt, etc.)
- Video overview & format
- Introductory Behavioral questions | Data science interview
- Social media platform bot issue task overview | Data science interview
- What are some features we should investigate regarding the bot issue? | Data science interview
- Classification model implementation details (using feature vectors) | Data science interview
- What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview
- Technical implementation details (python libraries, cloud services, etc) | Data science interview
- Any questions for me? | Data science interview
- Post-interview breakdown & analysis
- Video Introduction
- How podcasts work (RSS feeds overview)
- How can we utilize the XML webpages? (breakdown of RSS feed information & how we’ll use it to create a smart program)
- Accessing this project on GitHub
-Writing Python code to download podcasts locally (requests & beautifulsoup libraries)
- Modify our script to be able to download many podcasts
- Building in smart search capabilities to grab podcasts we’ll find most interesting!
- Using the AssemblyAI API to transcribe the podcasts we’ve downloaded
- Cleaning our code with functions & classes and putting everything into Python scripts.
- Portfolio project extension ideas! (Spotify API, NLP semantic search)
- Smash like & subscribe pretty please :)
- Intro & Video Overview
- Check out this Video’s Sponsor, Brilliant!
- Coding #1 (Microsoft, Easy) - Finding Updated Records
- Coding #2 (Airbnb, Easy) - Number of Bathrooms and Bedrooms
- Coding #3 (Google, Medium) - Counting Instances in Text
I know it's more a reference to the stock market terms, but I can't stop thinking of Fallout: New Vegas.
- Coding #4 (Meta/Facebook, Medium) - Customer Revenue in March
That first one and others are SQL problems converted to pandas. I suppose that's a decent way to get basic pd questions. ()
- Coding #5 (Amazon, Hard) - Monthly Percentage Difference
AtI work for Amazon's RPA team, trying to make a career in data science. Last month I was appearing for an IJP and got the same question in SQL coding round.Thanks for making this Keith. Keep them coming.
- Coding #6 (Microsoft, Hard) - Premium vs Freemium
- Non-Coding #1 (Visa, Easy) - Credit Card Activity
If you have the locations that's just a simple matter of putting it on a map and seeing where it clusters the most.
- Non-Coding #2 (IBM, Easy) - Outliers Detection
- Non-Coding #3 (Google, Medium) - Probability of Having a Sister
- Non-Coding #4 (Uber, Medium) - Uber Black Rides
Context, context, context. Was that the only reduction?
- Non-Coding #5 (Capital One, Hard) - Terabyte of Data
- Video Conclusion & Recap
- Introduction & video overview
- Shoutout to this video’s sponsor, Brilliant.org!
- 1. Running terminal commands such as “pip” directly in a notebook
- Magic lines in Jupyter
- 2. Shortcuts that you need to know for Jupyter!
Pretty unnecessary to write all these out. Just go to the “Help” menu, and select “Keyboard Shortcuts”. They’re all there—and more.
- 3. Changing default Pandas options to improve results display
- 4. Setting up notifications for when a cell finishes execution
- 5. Creating slideshows from a IPython notebook!
- Conclusion (and link to bonus tip!)
- Intro
- Video overview (what we’ll be working on)
- Code setup (GitHub repo & HP challenge link)
- Exploring the dataset that we’ll be using
- Reviewing template code (starter-code.ipynb)
- Installing necessary Python libraries (opencv-python, tensorflow)
- Reviewing template code (part 2)
- How we load in the dataset (ImageDataGenerator, flow_from_directory)
- Building our first classifier (convolutional neural net - CNN)
Buy more GPUs.
- Methods to improve neural network performance (MaxPooling, dropout, network architecture)
- Quick discussion about importance of precision & recall versus accuracy
- Data augmentation & preprocessing (another way to improve performance)
- Programmatically finding the best neural network architectures (Keras Tuner)