Experimented with Teachable Machine today and created a #nocode classification model in less than 5 mins!

It's a web-based tool making #machinelearning models fast, easy, and accessible to everyone.

See how I did it 🧵👇

teachablemachine.withgoogle.com

#nocode #31DaysofML
How do I use it?

📌Gather data (upload it)
📌Train model (in the web interface)
📌Export the model (use it in your app) Image
What can I use to teach it?

📌Images
📌Sounds
📌Poses

We can use files or capture examples live through webcam/microphone. Image
What did I do with it?

I trained a model to classify yoga poses by uploading images of the different yoga poses. Image
Now, get out of the chair and try out my yoga pose classification model with either of these poses:
📌Goddess
📌Warrior2
📌Tree
📌Downdog

teachablemachine.withgoogle.com/models/vkzpnwt…

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More from @pvergadia

15 Feb
Day 14 #31DaysofML

🤔 How to pick the right #GoogleCloud #MachineLearning tool for your application?

Answer these questions
❓ What's your teams ML expertise?
❓ How much control/abstraction do you need?
❓ Would you like to handle the infrastructure components?

🧵 👇
@SRobTweets created this pyramid to explain the idea.
As you move up the pyramid, less ML expertise is required, and you also don’t need to worry as much about the infrastructure behind your model.

To lear more watch this video 👉

#31DaysofML 2/10
@SRobTweets If you’re using Open source ML frameworks (#TensorFlow) to build the models, you get the flexibility of moving your workloads across different development & deployment environments. But, you need to manage all the infrastructure yourself for training & serving

#31DaysofML 3/10
Read 10 tweets
14 Feb
Day 13 #31DaysofML

⚖️ How to deal with imbalanced datasets?⚖️
Most real-world datasets are not perfectly balanced. If 90% of your dataset belongs to one class, & only 10% to the other, how can you prevent your model from predicting the majority class 90% of the time?

🧵 👇
🐱🐱🐱🐱🐱🐱🐱🐱🐱🐶 (90:10)
💳 💳 💳 💳 💳 💳 💳 💳 💳 ⚠️ (90:10)
There can be many reasons for imbalanced data. First step is to see if it's possible to collect more data. If you're working with all the data that's available, these 👇 techniques can help

#31DaysofML 2/7
Here are 3 techniques for addressing data imbalance. You can use just one of these or all of them together:
⚖️ Downsampling
⚖️ Upsampling
⚖️ Weighted classes

#31DaysofML 3/7
Read 7 tweets
3 Feb
👋Day 2 #31DayofML

Let's explore #MachineLearning terms for supervised learning:
🔸Labels - the thing we're predicting
🔸Features - an input variable
🔸Examples - particular instance of data (Labeled/Unlabeled)
🔸Models - defines the relationship between features & label.

A 🧵
🔸Labels - the thing we're predicting

Eg: The y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.

#31DayofML
🔸Features - an input variable x in linear regression.

A simple ML project might use 1 feature X, while a sophisticated project could use millions of features X1, X2, ..... Xn

Eg: In the spam detector, features could be:
📌Words in email
📌Sender's address
📌Time
📌Some phrase
Read 5 tweets
2 Feb
🏹 Let's go Day 1 of #31DaysofML

💡What is #MachineLearning? 💡

ML = Using data to answer questions!
📌 Using data = Training
📌 Answer questions = Predictions

Let's keep going... 🧵👇
2/4 What are the 7 steps in Machine Learning?

1️⃣ Collect Data
2️⃣ Prepare Data
3️⃣ Choose a Model
4️⃣ Train the Model
5️⃣ Evaluate the Model
6️⃣ Parameter Tuning
7️⃣ Make Predictions

For more @yufengg amazing video 👉bit.ly/3j3j2ne

#31DaysofML
@yufengg 1️⃣ Collect Data

📌Quantity & quality of your data dictate how accurate our model is
📌The outcome of this step is usually a table with some values (features)
📌 If you want to use pre-collected data - get it from sources such as Kaggle or BigQuery Public Datasets

#31DaysofML
Read 9 tweets

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