Complete Python , AI Agents : All Level Mega Pack 2026

Learn to control Python program execution using if, elif, else, for and while loops, and master break and continue to direct flow with practical examples.

Learn how to create and call functions in python, including user defined and built in functions, with def syntax, parameters, and lambda expressions for reusable code.

Explore file handling in Python by opening, reading, writing, appending, creating, and deleting files, using modes r, a, w, x, and the os module.

Explore numpy, the essential python library for linear algebra, and learn to create 1d and 2d arrays in a Jupyter notebook by importing numpy as np and using np.array.

Explore methods to initialize numpy arrays, including zeros, arange, linspace, full, and random values, with practical code examples in Python using numpy.

Explore inspecting and reshaping numpy arrays, including shape, ndim, size, and dtype, with hands-on examples of resizing arrays and counting elements.

Learn how to manipulate numpy arrays in Python by concatenating arrays, stacking them vertically or horizontally, column-wise stacking, and splitting arrays using numpy split on a 4x4 example.

Master indexing and slicing in NumPy to select elements from arrays using 0-based and negative indices, and practice slicing across rows and columns.

Compare numpy arrays with Python lists, showing memory efficiency, faster performance, and greater convenience in scientific computation, through practical in-notebook examples and memory and speed measurements.

Explore SciPy, the free open source Python library built on NumPy, offering modules for optimization, linear algebra, integration, interpolation, special functions, Fourier transform, and signal and image processing.

Master the SciPy cluster subpackage to perform k-means clustering, assign data to the nearest centroid, update centroids, and use data whitening with a car dataset.

Explore pandas, the open source python library for data manipulation and analysis, covering series and data frames, missing data as NaN, group by, slicing, merging, reshaping, and time series tools.

learn what a pandas data frame is—a two-dimensional, labeled structure with columns of different types—and how to create one from lists, dictionaries, a series, or a numpy array.

Import and analyze a data set with pandas by loading a csv into a data frame and using head, tail, shape, and describe.

Cleaning the data set involves detecting and correcting or removing corrupt records, renaming columns, filling null values with the mean, dropping unused columns, and analyzing correlations.

Manipulate the data set with pandas by indexing with iloc and loc, viewing and slicing columns, setting values, applying lambda functions, sorting, and filtering by cylinders and horsepower.

Explore merging, joining, and concatenating data frames in pandas, performing inner, left, right, and outer joins, and comparing merge versus join on index versus columns.

Visualize a dataset using line, area, and bar plots with matplotlib to reveal horsepower trends and displacement comparisons across x coordinates.

Discover how data visualization transforms data into visuals that reveal patterns, such as Anscombe's quartet, enabling better decisions. Explore Python libraries like matplotlib, seaborn, ggplot, bokeh, and plotly.

Explore Matplotlib, a Python library for visualizing data and creating plots with pyplot for customization. It supports line, bar, scatter, histogram, and pie plots, exporting to PNG or GIF.

Learn to create and customize a scatter plot with matplotlib, adding legends, titles, axis labels, colors, sizes, markers, edge colors, alpha, grids, and saving the figure.

Learn to create and customize a histogram in Python using matplotlib, including data preparation, defining bins, labeling axes, coloring, grid, and saving the figure.

Create and customize pie charts and donut charts in python with matplotlib, using labeled data, percentages, shadows, and a centered white circle for donuts.

Explore the basics of data science, including qualitative and quantitative data, and classify data types: discrete, continuous, categorical, and ordinal. Learn how statistical methods reveal insights for decision making.

Explore basic statistics as the descriptive framework for data, covering mean, median, mode, variance, and standard deviation, and see how statistics drive data science and machine learning in real-world scenarios.

Explore sampling by defining population and sample, then compare probability-based and non-probability-based techniques with examples of simple random, systematic, stratified, cluster, convenience, purposive, snowball, and quota sampling.

Explore basic probability for data science, including experiments, outcomes, and marginal, joint, and conditional probabilities, plus Bayes' theorem with practical dice and card examples.

Explore probability distributions and their properties, including the normal (Gaussian) distribution, probability mass and density functions, z-scores, and the central limit theorem.

Explore inferential statistics for data science, including confidence intervals, hypothesis testing, t and z tests, F tests, chi-square tests, and ANOVA, with practical Python examples.

Explore the three main types of machine learning: supervised, unsupervised, and reinforcement, and see real-world use cases like spam detection, Netflix recommendations, and self-driving cars.

Discover what you can ask with classification, anomaly detection, clustering, and regression algorithms. Ask if mail is spam, detect fraud, cluster customers, or gauge house value or rain tomorrow.

Explore an end-to-end machine learning project in Python using the iris dataset, loading and summarizing data, visualizing features, evaluating multiple models, and making predictions to select the best performer.