Discover and read the best of Twitter Threads about #hubofml
Most recents (5)
✅Linear Neural Networks for Regression and Classification explained in simple terms and how to use it (with code).
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : Joshua
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : Joshua
1/ Imagine you have a box with a lot of buttons on it, and each button can give you a different answer. You also have a big list of questions that you want to ask, like "What's the weather like today?" or "Is this a cat or a dog?".
2/ Now, instead of asking just one button at a time, you can connect all the buttons together with wires. When you ask a question, it goes through the wires and each button can help decide the answer a little bit.
✅Time Series Forecasting explained in simple terms and how to use it ( with code).
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
1/ Imagine you have a special notebook where you write down the temperature outside every day. You write down the temperature in the morning and also in the afternoon. Now, after a few months, you have a lot of temperature numbers in your notebook.
2/ Time series forecasting is like using magic to predict what the temperature might be in the future. You look at all the numbers you wrote down and try to find a pattern or a trend.
✅Generative Adversarial Networks ( GANs) explained in simple terms and how to use it ( with code).
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
1/ Imagine you have two friends, let's call them the "artist" and the "critic." The artist wants to draw something cool, and the critic wants to judge if the drawing is good or not. The artist tries to draw something, and the critic looks at it and says whether it's good or bad.
2/ Now, the artist really wants to improve, so they keep drawing and the critic keeps judging. Over time, the artist gets better and better at drawing because they learn from the critic's feedback. The artist wants to make drawings that the critic will say are really amazing!
Quick Thread : 5 Cool Advanced Pandas Techniques for Data Scientists
🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #Pandas
🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #Pandas
1. Split data using pandas
In the code below, we are splitting the data into a random sample of rows and removing them from the original data after dropping index values.
In the code below, we are splitting the data into a random sample of rows and removing them from the original data after dropping index values.
2. Binning Data
Binning is a technique to group/bin your data into multiple buckets which is very helpful if you dealing with continuous numeric data. In pandas you can bin the data using functions cut and cut. First check the shape of your data i.e no of rows and columns.
Binning is a technique to group/bin your data into multiple buckets which is very helpful if you dealing with continuous numeric data. In pandas you can bin the data using functions cut and cut. First check the shape of your data i.e no of rows and columns.
Pandas is a fast, powerful, flexible and open source data analysis and manipulation tool.
A Mega thread 🧵covering 10 amazing Pandas hacks and how to efficiently use it(with Code Implementation)👇🏻
#Python #DataScientist #Programming #MachineLearning #100DaysofCode #DataScience
A Mega thread 🧵covering 10 amazing Pandas hacks and how to efficiently use it(with Code Implementation)👇🏻
#Python #DataScientist #Programming #MachineLearning #100DaysofCode #DataScience
1/ Indexing data frames
Indexing means to selecting all/particular rows and columns of data from a DataFrame. In pandas it can be done using two constructs —
.loc() : location based
It has methods like scalar label, list of labels, slice object etc
.iloc() : Interger based
Indexing means to selecting all/particular rows and columns of data from a DataFrame. In pandas it can be done using two constructs —
.loc() : location based
It has methods like scalar label, list of labels, slice object etc
.iloc() : Interger based
2/ Slicing data frames
In order to slice by labels you can use loc() attribute of the DataFrame.
Implementation —
In order to slice by labels you can use loc() attribute of the DataFrame.
Implementation —
