Top Tweets for #31DaysofML
Day 23 #31DaysofML by @pvergadia
💡Tips to get started with #MachineLearning💡
Pratham Prasoon is a 16 year old with passion for making #ML simple. We sat down for a virtual chat where he shares some of his favorite resources. https://t.co/ZK548CUqnC
💪We did it! It's Day 3️⃣1⃣ of #31DaysofML
I want to leave you will this difference between AI, ML and Deep Learning. We covered this on Day 11 but most of you asked for a video. So here you go!
https://t.co/xNYFmRFNWu
#ArtificialIntelligence #MachineLearning #DeepLearning
🔊 So excited to share this with you on day 30 #31DaysofML
Built a sound classification model for these sounds
🎸Guitar
🐦 Bird
👏 Clap
Also deployed the model on Cloud Run!
Want to try it yourself? code 👉 https://t.co/t4q2ESx78a
#MachineLearning #NoCode #GoogleCloud
Day 16 of #31DaysofML
Here are some use cases of computer vision
✅ Industrial inspection
👗 Product search
📃 Document classification
🖼 Image search
Checkout the architectures to implement using Vision API with
#NoCode #MachineLearning https://t.co/MeATuoLEyM
Day 29 #31DaysofML
🤔 What is Hyperparameter tuning?
The process of selecting the right set of hyperparameters for your #ML app
🤔 What are hyperparameters?
Variables that govern the training process & the topology of an ML model.
A 🧵 👇 1/3
Day 26 #31DaysofML by @pvergadia
🤔 What does MLOps looks like on #GoogleCloud?
👉 Checkout out this solution https://t.co/SI4ymyU9GK
https://t.co/eda6muaam5
Day 28 #31DaysofML
Sharing few resources for running #TensorFlow on @GoogleCloudTech
📌 Course 🔗 https://t.co/FDO8bYMOEd
📌 TF Deep Learning VM 🔗 https://t.co/Ur2cCTKxtn
📌 Train/predict with Keras 🔗 https://t.co/u28XlRHESI
📌 Distributed TF on GCE 🔗 https://t.co/3VaXuKch5F
Day 27 #31DaysofML
Here is a resource that can help architect a #serverless #MachineLearning model.
👉 https://t.co/OYiual9yOe
#GoogleCloud
Here is how #GoogleCloud AutoML helps make MLOps a bit easier. To learn more checkout this solutions 👉 https://t.co/zx0YY4HYiE
#31DaysofML
Day 26 #31DaysofML
🤔 What is MLOps?
#MachineLearning Model Operationalization Management
🧐 But what is it?
End-to-end #ML development process to design, build & manage reproducible, testable, & evolvable ML-powered systems
🤓 Here's how MLOps looks like on #GoogleCloud
1/2
Day 22 #31DaysofML
What is Scikit-learn?
It is #MachineLearning library that offers a rich suite of tools for doing things such as:
🔹 Dataset loading & manipulation
🔹 Preprocessing pipelines and metrics
🔹 Comes with large number of ML algorithms
https://t.co/mNDtamfrCo
Day 16 of #31DaysofML
Here are some use cases of computer vision
✅ Industrial inspection
👗 Product search
📃 Document classification
🖼 Image search
Checkout the architectures to implement using Vision API with
#NoCode #MachineLearning https://t.co/je3jath1sR
💡Tips to get started with #MachineLearning💡
A lot of us are fascinated by @PrasoonPratham who is on a mission to make #ML simple. So, for today's #31DaysofML
I invited him for a quick virtual chat. Watch & follow 📹 👉 https://t.co/amW2bUWvbz
Day 23 #31DaysofML
Today I created a tweet sentiment analysis model with absolutely #nocode using #GoogleCloud Natural Language AutoML which supports 👇 types datasets:
🔹 Single & Multi Label classification
🔹 Entity extraction
🔹 Sentiment Analysis
See how I did 👇
👉 Here is how you train the model
from sklearn import svm
clf = svm.SVC() --- Support vector classifier model
https://t.co/BsNiFyGKHQ(X_train, y_train) -- train the model using fit function
#31DaysofML
👉 Here is how you split the data using scikit-learn
from sklearn.model_selection import train_test_split
all_X, all_y = preprocess(data)
X_train, X_test, y_train, y_test = train_test_split(all_X, all_y)
#31DaysofML
Scikit-learn is also a great way to learn what different types of models do and gain some intuition around how the various parameters for a model perform.
#31DaysofML
Day 16 of #31DaysofML
Here are some use cases of computer vision
✅ Industrial inspection
👗 Product search
📃 Document classification
🖼 Image search
Checkout the architectures to implement using Vision API with
#NoCode #MachineLearning https://t.co/1ZzHfkCNPo
Day 18 #31DaysofML by Priyanka Vergadia (@pvergadia)
#machineengineering #ai #datascience #GoogleCloud #bigquery https://t.co/L7pcHxe0OK
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