Python-Introduction to Data Science and Machine learning A-Z

Learn the basics of statistics by exploring median, mode, and mean with clear definitions and practical examples. See how these formulas reveal central tendency in datasets.

Explore how to compute range, quartiles (Q1, Q2, Q3) and percentiles, and grasp standard deviation and variance to measure data dispersion in practical datasets.

Explore the basics of decision trees in data science and machine learning, including entropy and information gain, with simple examples. Relate them to brain-like decisions that guide data splits.

Import numbers as a feature and define one dimensional areas and two dimensional areas using Python, then print and add these areas to perform basic linear algebra.

Explore basic matrix operations in Python with arrays, including addition, subtraction, multiplication, and division, reshape arrays into a single column, sum across axes, and compute square roots and standard deviation.

Explore the basics of data analysis with Python and Pandas, including the data lifecycle, transforming raw data, and drawing insights using Pandas' powerful features.

Set up pandas in your text editor by configuring the project interpreter, installing the pandas package, and importing pandas to start data analysis, time series, and statistics.

Discover the basics of SciPy, an open-source Python library that extends NumPy with modules for data analysis, including linear algebra, integration, interpolation, clustering, and differential equation solving.

Learn basic SciPy operations in Python by importing packages, using from-import and import statements, and exploring help, info, and source to inspect package content.

Apply a SciPy fftpack transformation to a 2d array in Python by importing NumPy and SciPy, creating the array, and printing the transformed results.

Explore SciPy's linear algebra tools to solve linear equations and compute the inverse of a matrix using two square arrays, from the linear algebra subpackage.

Learn to visualize data with matplotlib in Python, using bar charts and scatter plots to reveal patterns in data. Grasp how matplotlib provides Matlab-like plotting library for data science visuals.

Explore how to visualize data with matplotlib by building a three-series bar graph, including data setup, axis labeling, and styling options.

Create a pie chart using matplotlib to visualize food sales by category, assigning labels and colors, and customize the style to compare pizza, ice cream, and burgers.

Create a scatter plot in matplotlib by supplying x and y data, adding a title and axis labels, and explore styling and multi-graph options.

Learn how Seaborn, a higher-level visualization library built on matplotlib, creates attractive statistical graphs with multiple variables, grids, colors, distributions, and automatic linear regression plots from the web.

Set up Seabourne in your text editor and install the Seabourne package to begin plotting. Import numpy, pandas, and Seabourne to create plots and graphs in your data science workflow.

Learn to create a Seaborn joint plot to explore the relationship between tips and total bill using the tips dataset, showing how higher bills relate to higher tips.

Visualize flight passenger data with seaborn to identify the most popular month, July, using a categorical plot of months vs passengers and exploring strip, violin, and box styles.

Explore seaborn's multi-plot visualizations by creating a face grid of scatter plots from the tips dataset, showing total bill, tip, sex, and smoker status.

Explore core machine learning algorithms with a theoretical introduction to linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means, and random forests, using Python.

Learn logistic regression using a beginner-friendly library, build a data frame with finance, management, and logistics features, and evaluate with train-test split, confusion matrix, and accuracy score.