Day 41 of #60daysOfMachineLearning

πŸ”· Machine Learning πŸ”·

Machine learning is a type of artificial intelligence that involves giving computers the ability to learn from data without being explicitly programmed.
This is achieved by training the computer on a large amount of data, and then allowing it to use what it has learned to make predictions or take actions on new data.
In other words, it is a method of teaching a computer to make decisions or take actions based on the data it has been given, rather than following a set of rules or instructions provided by a human.
This can be a powerful tool for solving complex problems, and it is being used in a wide range of applications, from self-driving cars to medical diagnosis.
Machine learning allows us to make better decisions, faster and more accurately, by harnessing the power of data and algorithms. It is a key part of the future of artificial intelligence, and it is already changing the way we live and work.
If you missed the previous days, don't worry! You can follow along and go back to day 1 by going to this link πŸ‘‡
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More from @DanKornas

Dec 12
Which Machine Learning model should you use to solve a problem? πŸ€”

There are several factors to consider when choosing which machine learning (ML) model to use for a particular problem.

🧡 Here are some key factors to consider πŸ‘‡
The type of problem you are trying to solve: Different ML models are suited to different types of problems, such as regression, classification, or clustering. You should choose a model that is well-suited to the type of problem you are trying to solve.
The size and quality of the data you have available: Different ML models have different requirements and capabilities when it comes to the size and quality of the data they can handle. You should choose a model that is able to handle the size and quality of the data.
Read 6 tweets
Dec 12
Day 42 of #60daysOfMachineLearning

πŸ”· Supervised Learning πŸ”·

Supervised learning is a type of machine learning algorithm that uses labeled training data to learn a function that can map input data to the desired output. Image
In supervised learning, the training data consists of a set of input data and corresponding labels or output values, which are used to train the model to produce the desired output for a given input.
For example, in a supervised learning model for image classification, the training data would consist of a set of images and their corresponding labels, such as "dog", "cat", or "car".
Read 8 tweets
Dec 12
Day 42 of #60daysOfMachineLearning

πŸ”· Supervised Learning πŸ”·

Supervised learning is a type of machine learning algorithm that uses labeled training data to learn a function that can map input data to the desired output. Image
In supervised learning, the training data consists of a set of input data and corresponding labels or output values, which are used to train the model to produce the desired output for a given input.
For example, in a supervised learning model for image classification, the training data would consist of a set of images and their corresponding labels, such as "dog", "cat", or "car".
Read 8 tweets
Dec 10
Day 40 of #60daysOfMachineLearning

πŸ”· Pandas Plotting πŸ”·

Pandas also includes a number of functions for visualizing and plotting data. To create a basic line plot in Pandas, use the .plot() method on a dataframe. This method takes optional arguments to customize the appearance
For example, if you had a dataframe df with two columns x and y, you could create a line plot of these columns using the following code: Image
Pandas also provides convenience functions for creating common plot types, such as bar plots, histograms, and scatter plots Image
Read 5 tweets
Nov 29
Day 29 of #60daysOfMachineLearning

πŸ”· Data Visualization with Matplotlib

πŸ”΅Pie ChartsπŸ“Š

Lets create a pie chartπŸ‘‡

With Pyplot, you can use the pie() function to draw pie charts.

A simple pie chart: Image
πŸ”΅ Result

As you can see the pie chart draws one piece (called a wedge) for each value in the array (in this case [35, 25, 25, 15]). Image
πŸ”΅ Explode a Pie Chart

Maybe you want one of the wedges to stand out? The explode parameter allows you to do that.

The explode parameter, if specified, and not None, must be an array with one value for each wedge. Image
Read 7 tweets
Nov 28
You don't need to spend a penny to learn Machine Learning.

Here are 5 GitHub repositories to learn Machine Learning for free πŸ‘‡
Homemade Machine Learning

This repository contains examples of popular machine learning algorithms implemented in Python with mathematics behind them being explained.

github.com/trekhleb/homem…
Made With ML

Learn the foundations of machine learning through intuitive explanations, clean code and visualizations.

madewithml.com
Read 6 tweets

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