Python for AI and Machine Learning

Learn Python basics by understanding variables, dynamic typing, and case sensitivity, and explore data types such as integers, floats, complex numbers, booleans, strings, None, tuples, lists, and dictionaries.

Learn how Python control flow uses loops, conditional statements, and function calls to decide execution, with examples of if else, while and for loops, and Python's spacing over tabs.

Learn how Python functions act as blocks of code you call repeatedly, with and without arguments, using print to display messages like hello from Python tutorial.

Learn how to create and use modules in Python, import built-in and installed packages, and assemble a simple module to access functions like hello and plot with matplotlib.

Learn how lists, dictionaries, and sets empower data processing in ai and ml by wrangling data, normalizing sensor data, encoding labels, and deduplicating while preparing training and test sets.

Explore NumPy for scientific computation by creating arrays, computing max, min, mean, median, and sum, generating random numbers, and evaluating Pearson correlation coefficients.

Discover how to create, read, update, and delete files in Python using the open function and modes r, a, w, and x, with practical data.txt examples.

Explore managing directories in Python using the os module to get and change the current working directory, expand the home directory, list files, and check path existence.

Explore data manipulation with pandas to create data frames, filter with boolean conditions, and merge datasets for richer insights and cleaner features.

Import a csv into Jupyter Notebook, inspect for missing values and outliers, and clean with pandas to pre-process data, including converting health to binary (0 for unhealthy, 1 for healthy).

Master plotting in Python for ai and machine learning using matplotlib and seaborn, with line, bar, scatter, box, histogram, and time series charts, plus interactive Jupyter notebook workflows.

Explore advanced seaborn visualizations—heatmaps, swarm plots, bar plots (including stacked bars), pair plots, 3d scatter plots, and joint plots—using the flights data set and London Borough Profile data.

Train a simple random forest model on a crop health csv dataset and visualize performance with a confusion matrix and ROC curve to assess healthy versus unhealthy predictions.

Compare different classifiers on synthetic data using Python, including k nearest neighbors, decision tree, random forest, and neural network, with pipelines and accuracy results showing neural network leads.

Evaluate a crop health model using accuracy, precision, recall, and cross-validation on a random forest trained with ndvi and soil moisture data; cross-validation reveals overfitting and need for model simplification.

Perform hyperparameter tuning of a random forest in Python using grid search and randomized search in scikit-learn on the crop health CSV dataset, improving cross-validation scores and assessing model stability.

Build a convolutional neural network for satellite image classification using PyTorch, training on the Neurosat dataset with ten land-use classes and a pre-trained ResNet-50, 80/20 train-test split.

Set up a GPU-enabled TensorFlow 2.1 environment with CUDA toolkit. Verify GPU availability in Jupyter and compare GPU versus CPU performance on matrix multiplication and a neural network.

Explore Google Colab, a free cloud-based platform to write and run Python code in a collaborative Jupyter Notebook, with GPU/TPU access and Google Drive integration for data analysis.

Set up and use Google Colab for AI and machine learning projects, enabling GPU and TPU acceleration, running TensorFlow and PyTorch in your browser, with hands on notebooks and visuals.

Train TensorFlow and PyTorch models on Google Colab GPUs to classify crop health using two features, ndVi and soil moisture, with a two-layer neural network and Adam optimization.

Train a PyTorch neural network in Colab on 1000 two-feature samples to classify crops as healthy or unhealthy, using 16-neuron hidden layer and sigmoid output, with Adam and cross-entropy loss.

learn to save and share Google Colab notebooks by saving to Google Drive, downloading locally, and managing permissions for secure collaboration.

learn how to process remote sensing data with Python to compute vegetation indices like NDVI and train neural networks that classify crop health with high accuracy.

Download the script and data, set up python tools like pandas, matplotlib, seaborn, numpy, scikit-learn, and statsmodels, and analyze so2, no2, rsp, and sbm in india's air quality.

This course uses python for ai and ml to monitor air quality in India, remove outliers with the iqr method, impute missing data, and compute aqi from so2, no2, spm.

Perform exploratory data analysis with visualizations to uncover relationships between pollutants and AQI. Apply data preparation, backward elimination, and linear regression with cross-validation to build reliable AQI models.

Explore multicollinearity and VIF diagnostics in Python-based air quality models for India, apply stepwise feature selection, analyze interaction effects, and implement ridge regression with cross-validation to predict AQI.

Detect and count plants with Python by building a geospatial workflow using GeoPandas, Rasterio, and OpenCV; create a plant model, blob detector, binary mask, and coordinates.