Introduction To Machine Learning with Python for beginners

Master supervised learning, using labeled data to train models. Explore classification and regression with examples like spam filtering, disease prediction, weather, and predicting house prices as continuous values.

Explore unsupervised learning, where input data without labels reveals patterns and forms clusters, with examples like recommender systems and customer behavior analysis.

Master reinforcement learning, a trial-and-error, goal-based approach where an agent learns from actions and feedback through rewards and punishments, illustrated by Pacman.

Explore deep learning and artificial neural networks, including neurons, weights, and activation functions, and learn how ANNs tackle handwriting recognition, image compression, text-to-speech, and stock price prediction.

Explore supervised learning algorithms, including linear regression, logistic regression, decision trees, nearest neighbors, and naive Bayes, with concepts like regression targets, sigmoid probability, and model fitting.

Explore unsupervised learning through clustering, including k-means and fuzzy clustering, detailing cluster centers, membership degrees, and how fuzzy logic handles ambiguous classifications.

Learn reinforcement learning with q-learning, q values, states and actions, and temporal-difference updates, guided by gamma, alpha, and a greedy policy with epsilon.

Install Python and the Anaconda environment on Windows, Linux, and Mac, and run basic arithmetic in IDLE. Save and run Python scripts from the IDLE IDE.

Install python 3 on linux using sudo apt update and sudo apt install python3, install idle, create and run a simple python script to perform basic arithmetic and print results.

Install python on mac from the official website, complete the setup with admin password, and verify the installation in the terminal using python3 --version (shows 3.8.3) and basic commands.

Install and launch Anaconda Navigator on Windows 64-bit, rename your Python file, explore Jupiter notebook, and run simple calculations by executing cells with Shift+Enter.

Master step-by-step data acquisition for machine learning, including data discovery, collection, augmentation, and generation from public and internal sources, and prepping data into machine-ready formats.

Explore common data formats for machine learning, including CSV, Excel, and JSON, and learn how to manipulate and convert between these formats to prepare data.

Explore public data sources like Kaggle, Google datasets, and government portals, and learn dataset structure with rows, columns, features, and data points.

Explore data with exploratory data analysis to reveal dataset structure, identify variable types, missing values, outliers, and relationships, guiding feature engineering and model training for machine learning.

Learn how data visualization reveals patterns, trends, and correlations in Python, using libraries such as Matplotlib and Seaborn and the grammar of graphics approach with Jiblah.

Learn how to clean data by handling missing values, using deletion, imputation by median or mode, adding a new category for unknowns, or predicting missing values with suitable models.

Identify and clean outliers to prevent skewed measurements, using box plots, histograms, and scatterplots; learn causes, effects on mean and standard deviation, and common handling methods.

Data standardization rescales attributes to a mean of zero and a standard deviation of one, converting disparate data sets into a common format for consistent preparation and analysis.

Learn how data normalization scales features to a common range, enabling fair comparisons and stable model training. Explore simple feature scaling, min-max, and standard score methods with examples.

Import and clean Titanic data with exploratory analysis, visualize survival trends by gender and class, and map titles and ages to prepare a clean dataset for machine learning.

Select the best model using cross-validation across classification, clustering, and regression. Split data into folds, train on some, test on others, and choose the model with the lowest cross-validation error.

Split data into training and test sets to train models on features and answers, then evaluate performance on unseen data using 80-20 or random splits.

In this lab, select and train a model on the Titanic survivors dataset with preprocessing and feature engineering, comparing logistic regression, decision tree, k-nearest neighbors, and naive bayes.

Learn to evaluate machine learning models with k-fold cross-validation to estimate accuracy on independent data. The module covers training and validation splits, multiple rounds, and averaging results to reduce variance.

Define accuracy as closeness to the actual value and precision as consistency across measurements, and explain recall and false negatives, with false positives, using fraud detection as an example.

In this lab, evaluate logistic regression and other models using 10-fold cross-validation with accuracy, compare confusion matrices, and conclude logistic regression best predicts survivors.

Learn how to deploy machine learning models into production, ensuring portability and scalability, via data, feature, scoring, and evaluation layers, with one-off, batch, and real-time deployment options and latency considerations.

This capstone project explores the Boston housing dataset, analyzes features and outliers, imputes data, and compares linear regression and a decision tree regressor on 70/30 split to predict house prices.