Learn Machine Learning & Data Mining in Python

Learn to create lists in Python with numbers, text, or mixed values, then use len, sort, and append, and access or extract values via indexing.

Learn how to create tuples and dictionaries in Python, explore tuples' immutability, access values by index or key, and build dictionaries with curly braces and key-value pairs.

Learn how to implement loops and functions in Python, including for and while loops, range, conditionals, and simple functions to manipulate lists and data.

Learn to use NumPy library for working with arrays and multidimensional arrays like matrices in Python.

Import numpy in a notebook (Colab) as np, create a normal-distribution dataset, and compute its mean. Then import matplotlib.pyplot as plt to plot a histogram inline.

Explore the scikit-learn library, its tools for classification, regression, clustering, and dimensionality reduction. Import the library to start building machine learning models in this course.

Explore supervised learning, where models use known labels to classify or predict, distinguishing classification for categorical outcomes from regression for continuous values with features like height, weight, size, and location.

Learn how r-squared quantifies the portion of the dependent variable's variance explained by predictors, interpret 0–100% values, and note the role of adjusted r-squared in model evaluation.

Learn how to assess predictor significance in regression using p values and a 0.05 threshold, deciding when to keep or remove variables like price, model, year, mileage, age, and gender.

Learn simple linear regression with one independent variable, Y hat equals b0 plus b1 X, and how X predicts Y, for example year of experience to predict salary.

Analyze a dataset of 30 students with hours studied and exam scores, and build a simple linear regression model to predict scores from study time.

Learn to prepare data for a simple linear regression in Python by extracting X and Y with iloc, viewing their values, and splitting data into train and test sets.

Build and evaluate a simple linear regression model in Python by predicting exam scores from study hours and comparing to actual values. Visualize with a regression line and scatter plot.

Discover stepwise regression in multiple linear regression, using forward selection and backward elimination to keep significant variables, guided by p-values and R-squared and adjusted R-squared for model quality.

Explore polynomial regression as an extension of linear regression, modeling curved relationships between inputs and output by using powers of input terms and their coefficients.

Learn logistic regression's binary prediction with a sigmoid function and use age, years as a customer, and gender to estimate buying probability above 50 percent.

Analyze a bank's client data with features like age, accommodation, income, gender, household size, credit lines, and loans. Build a logistic model to predict loan defaults and identify future applicants.

Learn how the k nearest neighbors algorithm classifies a new point by using a selected k and distance measurements, including euclidean distance and collodion methods, with bank client examples.

Explore the random forest classifier, a collection of decision trees. Create nine trees, average their predictions, and experiment with different tree counts in Python to maximize prediction power.

Explore the concept of unsupervised learning, where data lacks predefined targets, and cluster data to discover groupings such as customer segments by location or purchasing behavior.

Learn how a dendrogram guides the optimal number of clusters by mapping pairwise distances with vertical and horizontal lines and cutting at the longest non-crossing vertical line.

Explore a dataset of around 100 movies with budget, genre, and worldwide gross, clustering films to uncover hidden patterns and prepare for unsupervised learning techniques.

learn to build a k-means clustering model in python by loading the dataset, extracting two inputs, and plotting the elbow method to determine the optimal number of clusters.

Apply k-means clustering in python by setting n_clusters=3, init='k-means++', and random_state=0; predict on X, and display a three-cluster scatter plot with labeled clusters.

Discover association rules and market basket analysis, illustrate left-hand side implies right-hand side, and explain support, confidence, and lift with retail examples like coffee and sugar and beer and diapers.

Investigate association rules on a 100-transaction convenience store dataset, treating each basket as a transaction and using a primary algorithm to uncover associated items.

Install and import the Pury library, load the 100-transaction dataset with five items per basket, and transform each row into a list of item strings for the apriori model.

Create association rules for market basket analysis in Python, identifying base items and add-ons, and evaluate support, confidence, and lift for jam with bread and coffee with donut.

Learn about loss functions, including mean squared error for regression and binary cross entropy or categorical cross entropy for classification, and how they guide weight updates.

Activation functions decide which nodes forward data through hidden layers to the output, shaping decisions across epochs; Relu for hidden layers, sigmoid for output, softmax for multiple outputs.

Explore TensorFlow, the open source deep learning library by Google, used in Python to build neural networks, with Keras on top for CPU and GPU speed.

See how a Python-based neural network iteratively improves from 38% to 80% accuracy, while using a confusion matrix to validate diabetic vs not diabetic predictions.

Explore statistics as the science of collecting, analyzing, interpreting, and presenting empirical data and methods for driving decisions, with examples from medicine, exams, and pricing.

Explore when to use a sample versus a population, and how a random sample from the population ensures each member has an equal chance of being selected.

Explore descriptive statistics to describe and summarize data with mean, median, and variance, visualize frequency and time-series patterns, and learn inferential statistics to generalize from samples to populations.

Explore how standard deviation measures data dispersion around the mean, and how variance equals the square of the standard deviation, with sigma and s for population and sample.

Learn how symmetry and skewness shape data distributions, compare mean, median, and mode across left-skew, right-skew, and symmetric cases, and recognize outliers at distribution tails.

Compare point estimates and confidence intervals to understand accuracy. Higher confidence widens intervals—from 68–72 at 90% to 65–75 at 95% and 60–80 at 99%, reflecting the alpha error.

Calculate a confidence interval for a single sample with known population variance using z-values and a z-table, covering 90%, 95%, and 99% levels and rule of thumb for clinical studies.

Compute the confidence interval for clinic income in a city where the standard deviation is known, using the sample mean from 30 clinics and the z-margin of error, with alpha/2.

Explore the t distribution, or student distribution, which handles small samples with unknown population standard deviation, using the t table and degrees of freedom to form confidence intervals.

Explore confidence intervals for two dependent samples using a before-and-after medical study, such as testing a blood pressure medicine, with a 99 confidence level and Excel methods.

derive a confidence interval for the difference between two independent means with unknown but equal variances, using pooled standard deviation, for salaries of engineers in Dallas and New York City.

Explore one- and two-tailed hypothesis tests using Z and T distributions, and understand alpha as the significance level, type I and type II errors, with examples from medicine and finance.

Test the hypothesis that a medicine lowers blood pressure using two dependent samples before and after treatment, with a 1% significance level and 14 degrees of freedom, yielding no evidence.