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10 Data Science Libraries most beginners miss out on in Python which can make our lives so much easier and our codes so much more efficient.
After training our model and have predicted the outcomes, we need to evaluate the model's performance. And here comes our Confusion Matrix.
We will see what actually gradient Descent is and why it became popular and why most of the algorithms in AI and ML follow this technique.
Exploratory Data Analysis is an approach to discover the insights in the data. It is one of the best practices in data science today.
K-means clustering is a powerful unsupervised machine learning algorithm. It is used to solve many complex machine learning problems.
Matplotlib is the best library to plot graphs in Python. Learn about Plotting graphs using matplotlib and draw graphs as per your data
Functions help in saving a lot of time by reducing repetitive coding especially in EDA. Learn how to optimize exploratory Data Analysis
Learn about Cost Functions, Gradient Descent, its Python implementation, types, plotting, learning rates, local minima, and the pros and cons.
Understand the problem of overfitting in decision trees and learn to solve it by minimal cost-complexity pruning using Scikit-Learn in Python.
Python functions are a great tool to have in your data science arsenal. Here's an introduction on Python functions.
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