Machine Learning from the scratch using Python

This lecture introduces data science fundamentals, highlights Python as the primary tool, covers statistics, machine learning, deep learning, natural language processing, and guides installing Anaconda and launching Jupyter notebooks.

Define and store variables to hold values, perform simple arithmetic, and handle reassignment. Explore data types—integers, floats, strings, and booleans—and use the type function to inspect them.

Learn how to store and define data types in Python, including floats, integers, and strings, and master type conversion, string concatenation, and converting strings to numbers.

Reinforce Python basics through practice sessions on variables, data types, and arithmetic. Revise concepts, explore printing, strings, concatenation, and prepare for machine learning.

Python lists are introduced as a collection of values stored in square brackets, with examples of printing, mixing types, and nesting lists to store lists within lists.

Access list values by index using square brackets, learn zero-based indexing, and explore both positive and negative indexes.

Explore built-in Python functions, call them with examples like print, convert strings to int and float, and use max or min to process data.

learn to use the built-in max and min functions to find the largest or smallest value in a list, and evaluate expressions from inside out.

Master function arguments and keyword arguments in Python, including default behavior and reverse sorting. Apply min, max, and round to practice building a data science pipeline.

This lecture introduces Python string methods, demonstrates indexing to locate substrings, using find and index for first occurrences, and applying lower and upper case transformations.

Learn how to import Python packages, use from package import function syntax to access specific functions, and alias libraries like math, numpy, and seaborn for data science projects.

Explore subsetting arrays by index and using operators like less than, greater than, and equal to to print boolean results for each element.

Explore numpy arrays and element-wise operations like multiplication, and learn how mixing data types coerces all elements to strings, impacting subsetting and computations.

Master subsetting arrays with boolean indexing to filter elements by conditions like greater than three or six. Use bracket syntax and boolean masks to extract matching values and indices.