How to Automate Exploratory Data Analysis (EDA) ? - Part 1 https://youtu.be/tMquUTJ6yXU


You should know when you want to expedite data analysis 🧐 I strongly recommend you to use in your real world problems. This module will help you a lot

This website will help you learn probability and statistics, the most important topics in math for machine learning!

seeing-theory.brown.edu


Don’t forget to add in bookmarks 🔖

Deep learning activation functions made cool and cute

Learning path to mastering data engineering:

🔸 SQL
🔸 Git
🔸 Bash
🔸 PostgreSQL
🔸 Java, Scala
🔸 Python
🔸 Docker
🔸 AWS
🔸 Airflow
🔸 Kafka
🔸 Spark
🔸 Kubernetes

Learning path to mastering MLOps:

🔸 Linux
🔸 Python
🔸 Docker
🔸 AWS
🔸 Terraform
🔸 Kubernetes
🔸 Prometheus
🔸 Grafana
🔸 Kubeflow
🔸 CDK
🔸 Travis CI and Herokuapp
🔸 ML Flow
🔸 Airflow


Many more and listed few only for idea

#! File can be opened in various modes

r = read - Default mode
r+ = read + write
w = write
a = append
w+ = write + read
a+ = append + read
X - W ==?


rb - read only binary format
wb - write only binary format
ab - append only binary format
rb+ - read and write only in binary format
wb+ -write and read only in binary format
ab+ - append and read only mode


#! file basic operations
open
close

#! Check permissions for file
readable
writable
closed

#! read functions
read
readline
readlines

#! write functions
write
writelines

#! Postion of file pointes
seek
seekable
tell

Getting started with PyTorch for deep learning? Cover these fundamentals first:

🔸Tensors
🔸Datasets & DataLoaders
🔸Transforms
🔸Build Model
🔸Automatic Differentiation
🔸Optimization Loop
🔸Save, Load, & Use Model

always read official docs for gain more knowledge
https://pytorch.org/tutorials/beginner/basics/intro.html

re you a student who wants to harness data and tech to make a difference? Learn from industry experts, collaborate with students around the world and be in the running for up to $10,000 in cash prizes. Learn more at ey.com/datasciencechallenge

Today, I will be talking about AI/ML best practices on weekly datascience Dialogue - Edition 3

Lets learn Django RestFramework. I have alreay explained DRF archtecture here. Will be adding subsequent tutorial video everyday. Remember i dont want to post every youtube links here .... Maximum i will try share knowldge through text and other ways.

8 reasons machine learning projects fail——

🔸 Doing ML for wrong reasons
🔸 ML not needed
🔸 Bad data
🔸 Poor problem framing
🔸 Model ≠ product
🔸 Bad infrastructure
🔸 No trust from stakeholders
🔸 Production failures

Skills to impress data science employers:

🔸 Data engineering
🔸 Model deployment
🔸 Cloud-based services
🔸 Infrastructure as code tools
🔸 Communication and storytelling

The last one is especially important

AI investment in drug design and discovery increased significantly: “Drugs, Cancer, Molecular, Drug Discovery” received the greatest amount of private AI investment in 2020, with more than USD
13.8 billion, 4.5 times higher than 2019.

Don't have enough data to train your model? Fret not! Use the synthetic one!

👉🏼 Synthetic data is artificially generated data that is not collected from real world events! It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.

🧠 Synthetic data can be used for many applications:
- Privacy
- Removing Bias
- Balancing Datasets
- Augment Datasets

👉🏼 Where to generate it from and how?

Open Source Project YData Synthetic: This repository contains material on GANs for synthetic data generation, especially regular tabular data and time-series. It consists a set of different GAN architectures developed using Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures.

Link: https://github.com/ydataai/ydata-synthetic

Code generation using machine learning. You draw ✍️ and Machine can create a code for you.....

Did you know

For most Python implementations different threads do not execute at same time: they merely appear to.

Threads may be running on different processors but they merely appear to.

But how to achieve multiple tasks running simultaneously?

Just think 🤔 will share answer tomorrow if none responded to this.