Machine Learning with Python: Bootcamp + Real-World Projects

Kick off with Python fundamentals and data science libraries (NumPy, pandas, and matplotlib), then apply scikit-learn to build and evaluate regression, classification, clustering, and dimensionality reduction models in hands-on projects.

Learn how machine learning, a branch of AI, uses data and algorithms to imitate human learning and differ from traditional programming, including supervised, unsupervised, semi-supervised, and reinforcement learning.

Explore the life cycle of a machine learning project: load data with pandas from csv, databases, or cloud storage; split into train and test; visualize, preprocess, train models, and evaluate.

Learn numpy for scientific computing in Python, using multi-dimensional arrays to support element-wise operations, import as np, and inspect attributes like ndim, shape, size, dtype, item size, and n bytes.

Create NumPy arrays from Python sequences with np.array, choosing dtype and shape, and generate zeros, ones, and full arrays to form ndarrays with specific fill values.

Create numpy arrays from scratch using np.arange and np.linspace for linear and spaced data. Fill with random values (uniform or normal), generate random integers, and build identity or empty arrays.

Learn numpy array data types and how to index and slice, retrieve single values or rows and columns, use negative indexing, and create stepwise subsets with seeded random arrays.

Explore numpy array slicing that creates views, how to make explicit copies, reshape with compatible sizes, and concatenate or stack arrays using concatenate, vstack, and stack.

Master mathematical operations on numpy arrays using Python operators and numpy functions, apply boolean indexing to filter values, and use aggregation across axes (sum, var, std, min, max, argmin, argmax).

Explore the pandas library built on NumPy for data science and wrangling. Import as pd, check version, and create series and data frames from lists or dictionaries.

Learn how pandas dataframes are built from multiple series with column and index labels, create dataframes from dictionaries or numpy arrays, and inspect columns, index, and values.

Learn slicing and indexing in pandas, including explicit versus implicit indices and the loc and iloc indexers, and apply to series and data frames with boolean indexing and column selection.

Create a dataframe from the sklearn diabetes dataset using pandas, then inspect with head, tail, info, and describe, add a target column, and export or load as csv.

Learn to inspect a pandas data frame for duplicates and missing entries, perform group by on gender to compute mean, and use agg, reset index, copy, and drop columns.

Learn to sort a pandas data frame by bp and bmi, with single or multiple columns, choose ascending or descending order, replace values, and view df columns after loading csv.

Learn to create visualizations with matplotlib for data storytelling using pyplot. Import mpl, plot lines with plt.plot, and generate sine and cosine waves with numpy in a single figure.

Explore customizing line plots with Matplotlib by coloring using short codes, grayscale, hex, or names, and applying solid, dashed, dash-dot, and dotted styles; set axis limits, labels, and legends.

Master creating scatter plots with plt.plot and plt.scatter, building x and y with numpy for visual relationships. Use pandas data frames to plot scatter, bar, line, and histograms.

Use scikit-learn's StandardScaler to transform a data frame to zero mean and unit variance. The example demonstrates fitting the scaler and applying transform to prepare features for modeling.

Encode categorical data with label encoding, ordinal encoding, and one hot encoding. Use sklearn preprocessing to fit and transform columns, manage feature names, and drop the first category.

Explore feature scaling with min max scaling and standard scaling on specific columns, such as year. Use sklearn pipelines and column transformers to impute missing values and apply scaling.

Explore sklearn evaluation metrics for classification and regression, including accuracy, precision, recall, R2, MSE, and RMSE, with true versus predicted values.

Learn to perform regression with linear regression on housing data, loading housing.csv, splitting data into training and testing sets, visualizing scatter plots, and extracting model parameters like intercept and coefficient.

Demonstrates evaluating a linear regression model by predicting house prices from floor area, plotting results, and assessing performance with MSE, RMSE, and R2 score on training and testing data.

Extend the simple linear model with polynomial features and use fit_transform to create a polynomial regression dataset for higher-order modeling.

Learn polynomial regression by transforming data with polynomial features, fitting a linear regression model, and evaluating with rmse and r2, comparing to simple linear regression.

Builds a sklearn pipeline for polynomial regression by transforming data with polynomial features and fitting a linear regression model. Evaluates performance with cross-validation, RMSE, and R2 on train/test data.

Perform binary classification on diabetes data with a decision tree using glucose and BMI to predict the outcome. Learn data loading, preprocessing, and visualization in Python using pandas and plot_tree.

Evaluate a decision tree classifier with max depth three, observe Gini impurity reductions, generate predictions, compare cross-validation accuracy 71.25% to 75% with stratified splits, and mention random forest classifier.

Explore the random forest classifier, an ensemble of decision trees for classification, with hyperparameters like estimators and max depth tuned via gridsearchcv and cross-validation.

Apply SVC for classification with scaling via standard scaler and pipelines. Compare linear, polynomial, and RBF kernels and their cross-validation accuracy against decision trees and random forests.

Discover unsupervised learning with kmeans clustering on a two-feature dataset with no target variable, loading clustering.csv, checking for nulls, describing data, and plotting x1 versus x2 to identify three clusters.

Perform k means clustering with sklearn, initialize three clusters, fit to the data, obtain cluster centers and labels, and visualize with an elbow inertia plot to justify three clusters.

Learn dimensionality reduction techniques and hyperparameter tuning as you load the breast cancer dataset from sklearn, inspect features and targets, and apply stratified train-test splits.

Apply a scalable pipeline with standard scaling, PCA, and SVM to reduce 30 features to five while achieving about 97.1% accuracy and explaining 85% of the variance.

Explore hyperparameter tuning with grid search CV on an SVM pipeline, including a parameter grid for PCA components, kernel, gamma, and C. Also try random search CV.

Explore the machine learning lifecycle and key Python tools—NumPy, pandas, matplotlib, and scikit-learn—for data analysis, modeling, and evaluation, covering regression, classification, clustering, PCA, and hyperparameter tuning.