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Machine Learning India

15 Jun, 4 tweets, 2 min read

🙋🏻‍♀️ Free Machine Learning Course With Certification by Jovian: jovian.ai/learn/machine-…

Read this thread for details about this course! 🤓

#machinelearning #artificialintelligence #datascience #python

🦾 "Machine Learning with Python: Zero to GBMs" is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python and its ecosystem of ML libraries: scikit-learn, XGBoost, and LightGBM.

You will:
👉🏼 Watch hands-on coding-focused video tutorials.
👉🏼 Practice coding with cloud Jupyter notebooks.
👉🏼 Build an end-to-end real-world course project.
👉🏼 Earn a verified certificate of accomplishment.

You will work on 3 programming assignments and build a course project where you'll train machine learning models on a large real-world dataset! ALL FOR FREE! 😍

Register here, right away: jovian.ai/learn/machine-…

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More from @ml_india_

Machine Learning India

@ml_india_

17 Jun

When beginners start with #DataScience, the biggest pain point is finding good datasets for projects.
Luckily, there are many good public #datasets available! 📈🤓

Here are places you can find them! ⬇️⬇️⬇️

#Thread ☕

#MachineLearning #datascience #AI

(1/8)

@fastdotai

1️⃣ @fastdotai

Apart from providing amazing free courses, Fast AI has teamed up with AWS to provide free datasets for image classification, NLP, image localization and COCO projects.

👉 course.fast.ai/datasets

(2/8)

@fastdotai

@fastdotai 2️⃣ Awesome Public Datasets

This is a rich github repo of carefully curated datasets from 35+ domains.

👉github.com/awesomedata/aw…

(3/8)

Read 8 tweets

Machine Learning India

@ml_india_

16 Jun

NumPy is a powerful #python library that helps us compute operations on primarily numbers, faster. It is an important tool for data science. 💪

Here are 5 powerful #NumPy functions that will help you in your projects!

#Thread ☕

#MachineLearning #datascience #AI

Let's go! ⬇️

First let’s see the advantages of using NumPy : 📈

1️⃣ Uses low memory to store data.

2️⃣ We can create n-dimensional arrays.

3️⃣ Operations like indexing, broadcasting, slicing and matrix multiplication.

4️⃣ Finding elements in the array is easy.

5️⃣ Good documentation.

Function 1 : np.sort()

This returns a sorted copy of the array. The original array is not changed.

The arguments the function normally takes are -

1. The Array to be sorted.

2. Axis along which to sort.

3. The method/ algorithm used for sorting as “kind = ”.

Read 8 tweets

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