Introduction To Machine Learning with Python for beginners

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

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 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.

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.

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.

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.

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.

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.

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.