AI Mastery: Python’s Odyssey in Artificial Intelligence

Explore machine learning and deep learning concepts via hands-on Python projects, including live projects, using Jupyter Notebook, Seaborn, and essential mathematical statistics.

Explore machine learning with Python, covering the machine learning lifecycle and essential Python packages for ML, plus deep learning examples, coding challenges, and end-to-end projects.

Explore supervised and unsupervised learning, including regression, classification, and clustering, with examples like house price and diabetes prediction, and follow the machine learning life cycle from data preprocessing to retraining.

Explore data pre-processing and EDA, focusing on labeling, encoding categorical variables, and imputing or dropping missing values to determine the right problem type (regression, classification, or clustering).

Explore bias and variance in machine learning, explain data skew, training versus testing splits, and the bias-variance trade-off to prevent overfitting and underfitting.

Explore data preprocessing and statistical analysis to guide model selection, assessing feature importance and metrics like accuracy, precision, recall, and F1 within bias-variance tradeoffs.

Understand that model selection hinges on data type and business context, not just technical metrics, then evaluate performance across iterations using confusion matrices and business-focused visuals.

Master model monitoring across monthly releases, applying data pre-processing, model selection, and evaluation within the scikit-learn regression workflow, and retraining on new data to address underfitting or overfitting.

Learn to load data from csv or sklearn datasets, handle missing values with mean imputation, encode ocean proximity with label encoding, and standardize features for regression in a housing dataset.

Check visualizations with heatmaps, box plots, and pair plots. Prepare X and y from scale_df, train-test split, fit linear regression, and assess mean squared error and r-squared; compare with random forest.

Showcases predicting median house value from median income with a random forest regressor, evaluating mean squared error and r², visualizing results; covers iris classification with k-nearest neighbors and logistic regression.

Load iris.csv, inspect data with describe, shape, and info; visualize with seaborn pair plots, violin plots, and heat maps, dropping the id column. Explore pca and lda for dimensionality reduction.

Apply dimensionality reduction to the iris dataset by performing PCA and LDA to transform four features into two components. Visualize the reduced data to compare PCA and LDA results.

Learn model selection in a hands-on iris data workflow, including preparation of x and y, label encoding of species, and creating a stratified train-test split.

Learn to implement a k nearest neighbor classifier with scikit-learn, perform a train test split, train across a range of k, evaluate with accuracy and confusion matrix, and visualize results.

Assess classifier accuracy using train-test scores and visualizations, compare models like k-nearest neighbor and XGBoost, and learn deployment and monitoring with a pickle file on cloud services.

Learn clustering with k-means in unsupervised learning, use the elbow method to pick clusters, identify outliers, and perform basic data preparation and exploratory data analysis on a live dataset.

Analyze descriptive statistics with describe() to grasp quartiles, mean, standard deviation, and data range; then prepare for unsupervised learning by checking unique values and applying label encoding for clustering.

Apply label encoding to convert status types into numeric categories, scale features with min-max scaler, then build and evaluate a two-cluster k-means model using sklearn, inspecting inertia.

Evaluate the labeling accuracy and model performance by computing the accuracy score, explore hyperparameters with the elbow method on k-means, and select two clusters as a baseline.

Learn how a multi-layer perceptron uses hidden layers to map inputs to outputs via forward propagation. Use a cost function and gradient descent, tuning the learning rate for effective training.

Explore learning rate dynamics, back propagation, gradient descent, batch processing, and dropout in multilayer perceptrons, with emphasis on forward and backward passes, epochs, and batch normalization.

Explore k-means clustering, using inertia and the elbow method to select the number of clusters, with 3, 4, or 10, and assess accuracy within the machine learning lifecycle.

Explore binary classification with a TensorFlow model built via functional api, using dense layers, label encoding, train-test split, and binary cross-entropy to achieve high accuracy.

Extend binary classification to multi-class with softmax activation and sparse categorical cross entropy. Implement with iris data set, train-test split, argument max predictions, and evaluate accuracy.

Learn to load the mNIST data, inspect train and test shapes, and visualize digits using matplotlib's pyplot with subplots and grayscale mapping, paving the way to deep learning applications.