Machine Learning with Python: Data Science for Beginners

Explore the course module contents, set up Python with pip on Windows, Linux, or macOS, install packages, and build core programming fundamentals like functions and types for data science.

Review the popularity of Python as a powerful open-source language for data analysis and machine learning, and outline essential packages, tools, and basics to get started.

The lecture contrasts Python 2.7 and Python 3.x, noting major changes and limited backward compatibility. It covers library and third-party package support, syntax shifts, and production considerations on Linux.

Install and set up the Python data science stack using Anaconda or alternative packages, choose the right version for Windows, Mac, or Linux, and configure your IDE (Spyder) and environments.

Explore Python datatypes, including booleans, numbers, strings, and complex numbers, and core collections such as lists, dictionaries, and sets; learn how dictionaries use keys and how sets differ from lists.

Take a Spyder tour to navigate the project explorer, organize Python files and packages, use the editor and console, and explore three panes with built-in help for data science workflows.

Explore core Python datatypes: lists, sets, dictionaries, booleans, and strings, and learn how indexing, functions, and basic operations shape data handling for machine learning.

Explore numpy datatypes and multi-dimensional arrays, learn indexing and reshaping, and perform basic arithmetic and trigonometric operations using dummy data for practical data science tasks.

Explore pandas datatypes and dataframes, including column types, indexes, and loading data from disk into memory, then inspect the first rows and assess values and missing data.

Explore data engineering by manipulating columns and slicing datasets, creating transformed columns as needed. Group data to compute averages like satisfaction by group or department, using string and arithmetic operations.

Explore data engineering concepts in data engineering 2, focusing on column values, left and right joins, and examining the satisfaction index within data columns.

Learn data engineering techniques in Python by performing joins, concatenating across axes, grouping by, and pivoting to explore unique values and satisfaction levels.

Define and reuse Python functions to process data columns, apply a function like square across data with map and apply, and create new columns.

Explore data visualization with scatterplots and boxplots, learn to label x and y axes, use subplots and color markers, and interpret trends in data.

Explore exploratory data analysis, feature engineering, and hypothesis testing as core steps in the machine learning workflow, with sections on statistics and modeling essentials.

Introduce a machine learning methodology, outlining a five-service framework from problem identification to production. Highlight building features, combining models, and solving specific problems for customers.

Explore exploratory data analysis to uncover the empirical relationship between hours studied and scores, using a student dataset to assess this relationship and interpret results.

Explore univariate analysis by using simple charts and statistics to interpret single-variable data, including frequency, mean, median, min, max, and box plots.

Explore univariate analysis by calculating mean, standard deviation, skewness, and kurtosis, and visualize distributions with histograms and box plots to compare samples and populations.

Explore bivariate analysis to understand how two variables relate, using scatterplots and correlation coefficients to reveal positive, negative, or weak associations.

Explore feature engineering for machine learning by selecting, encoding, and combining numerical and categorical data, guided by domain expertise and parsimony.

Explore core statistical concepts like mean, median, mode, standard deviation, and normal distribution, and learn how sampling distributions connect samples to populations using z-scores and standard error.

Learn probability distributions, including binomial and normal distributions, and apply these concepts to data scenarios, leading to hypothesis testing.

Begin with an introduction to machine learning, covering supervised and unsupervised learning, core algorithms, and ensemble modeling techniques, reinforced by coding examples for practical understanding.

Explore what machine learning is and how systems learn from data through representation, evaluation, and optimization. Compare supervised and unsupervised learning, and discuss predictive tasks and improving performance with feedback.

Explore supervised learning by linking input to outcomes, using classification and regression to model relationships between independent variables and a dependent variable.

Learn how simple and multiple linear regression fit a best-fit line by minimizing distance from data points, and interpret the intercept and slope for models with multiple independent variables.

Explore a practical regression demo in Python, building a simple linear regression model from X to Y, using an object-oriented approach and core data handling concepts.

Explore binary classification in supervised learning with logistic regression for yes/no outcomes. Optimize a log-likelihood cost function using gradient descent and interpret log-odds to predict probabilities.

Explore classification with logistic regression through a hands-on demo that builds a predictive model using satisfaction and other features, estimates probabilities, and analyzes outcomes.

Explore how decision trees split data into subsets and grow from root to leaves. Learn entropy as a measure of uncertainty and guide splits for classification and regression trees.

Explore how decision trees handle categorical variables through encoding techniques such as one-hot encoding and binary encoding. Learn pre-processing steps to prepare features for classification in supervised learning.

Explore unsupervised learning through clustering, forming groups with minimal variance and maximal separation. Learn how to choose cluster counts and apply k-means and hierarchical clustering with centroids and Euclidean distance.

Explore unsupervised learning with a hands-on clustering demo, using datasets and features to form clusters, measure distances, and interpret the center of two different classes.

Explore association rules in unsupervised learning by analyzing item sets, transactions, and patterns. Understand metrics like support and confidence to reveal correlations that drive recommendations in data mining.

Explore regression evaluation metrics, including r-squared and mean squared error, and learn how to interpret model performance and error in real-world data science.

Compare two different metrics for evaluating regression models, interpret predictions and outputs, and understand trade-offs in model performance with R-squared as a key example.

Learn to evaluate binary classifiers with confusion matrix metrics, including accuracy and precision, and interpret true positives and false positives to improve model performance.

Explore evaluation metrics for classification models, including accuracy, false positives, and false negatives, and learn how to interpret these measures to compare model performance.

Explore regularization and hyperparameter tuning to control model complexity, handle noise and outliers, and improve classification performance. Learn how penalties, tree depth, and other hyperparameters shape learning outcomes.

Explore the bias-variance tradeoff in machine learning, examining how model assumptions, calibration, and performance interplay to find the sweet spot between bias and variance.

explore cross validation to gauge model performance by partitioning data into training and validation sets, creating multiple samples, and averaging results to identify the best model.

Explore hyperparameter tuning by applying grid search and randomized search to find the optimal values that maximize a chosen metric and model accuracy, while balancing tradeoffs among candidate values.

Explore cross-validation and hyperparameter tuning in practical machine learning workflows, using data partitions, model evaluation, and ensemble modeling to identify the best estimates.

Explore ensemble modeling by combining multiple models through bagging and boosting, using voting to improve predictions and learn from errors across diverse samples.

Explore random forest and bagging, building diverse trees through feature sampling and bootstrap samples, then aggregate predictions to improve classification.

Explore XGBoost as a boosting technique with a fast, sequential approach. Observe how boosting uses sequential learning to improve the process and performance.

Explore the rf and xgb demo to build and compare ensemble classifiers, tune estimators and depth, and assess accuracy on the data.

Explore the machine learning problem lifecycle from framing a problem and defining the statement to selecting approaches, evaluating solutions, and presenting an end-to-end project with real-world context.

Define the problem clearly by framing it from point a to point b, using a structured intuition competition approach to build a blueprint for data science and machine learning.

Develop a blueprint for a business solution by defining problems and designing and testing hypotheses across key factors to improve customer satisfaction and profits.

Explore and define a machine learning use case by analyzing historical data to identify profit-decline drivers and build a supervised model to predict customer subscription propensity for targeted marketing.

Explore customer data with exploratory data analysis to identify key attributes and relationships. Engineer features and preprocess data to model a binary loan outcome, guiding decisions with simple, beginner-friendly steps.

Develop machine learning models with a focus on balanced training data, evaluate using accuracy, and optimize to reduce false positives while improving overall performance.

Storyboarding frames machine learning problems by identifying key factors, selecting outcomes to measure with metrics, and planning deployment to address bias as models evolve.