Python with Machine Learning: Start Building AI Models Today

Explore object-oriented programming in Python by building classes and objects, defining attributes and methods, applying inheritance, and exploring polymorphism and encapsulation for scalable, reusable code.

Learn python error handling and exceptions with try, except, else, and finally through examples like division by zero, value error, and file not found when reading files.

Explore numpy for numerical operations and pandas for data manipulation, performing element-wise calculations, summing arrays, and building data frames from dictionaries to analyze names, ages, and salaries.

Explore data visualization with matplotlib and seaborn by creating a basic line plot and a polished bar plot, using sample data, custom titles, axes, and palettes.

Discover how to use Python regular expressions with the re module to search, match, find all matches and their positions, extract data, validate emails, split, and substitute text.

Explore data analysis with Python by loading and manipulating a pandas DataFrame, inspecting shape, columns, and descriptive statistics, and adding a pass column while filtering by age and score.

Master data cleaning and transformation with pandas in Python. Fill missing values with unknown, drop incomplete rows, create derived features, uppercase country codes, and save ready datasets for analysis.

Explore practical techniques for handling missing data and outliers to clean data for analysis and machine learning, including filling with the mean and replacing outliers using pandas and numpy.

Turn raw data into meaningful insights through feature engineering with pandas, extracting day of week, computing revenue growth, and labeling high or low revenue using a 1000 threshold.

Learn how to normalize and standardize data for machine learning models using pandas, minmax scaler and standard scaler, scaling features to 0–1 and to zero mean, unit variance.

Explore machine learning concepts, including supervised, unsupervised, and reinforcement learning, and see a Python logistic regression demo predicting pass or fail from study hours.

Explore supervised, unsupervised, and reinforcement learning with practical examples: predicting scores from study hours using linear regression, clustering with k-means, and reward-based action selection.

Execute the end-to-end machine learning workflow in Python by defining the problem, preprocessing data, training and evaluating the model, and deploying a house price predictor with linear regression.

Explore linear regression to learn the relationship between experience and salary using a small data set, pandas for data handling, and sklearn for model training and prediction.

Explore k nearest neighbors (KNN) for classification and regression using a fruit dataset with weight and color score, train a KNeighborsClassifier with 20 neighbors, and predict apples versus oranges.

Explore k-means clustering, an unsupervised learning algorithm, to group data into three clusters in Python and view cluster centers and labels for interpretation.

Evaluate model performance using accuracy, precision, recall, and F1-score, guided by the confusion matrix. Learn how these metrics reveal strengths and weaknesses beyond accuracy alone.

Explore cross-validation techniques to evaluate model performance and generalization, including holdout validation and 5-fold cross-validation, using a synthetic data regression example with linear regression.

Learn how to install TensorFlow and Keras, build a three-layer neural network with dense layers and a Softmax output using the sequential API, and train on the MNIST dataset.

Discover how convolutional neural networks process images to perform tasks like image recognition and object detection, using filters, conv2d, max pooling, flatten, dense layers, and softmax.