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.

Learn to set up and run Python in a Jupyter notebook, print results, and perform basic arithmetic with addition, subtraction, multiplication, division, exponentiation, and modulo.

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.

Learn to slice lists in Python by using an inclusive start and an exclusive end, with zero-based indexing and practical examples. Understand negative indices and end behavior.

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

Learn to access elements in a list of lists using indices and square brackets, extract the second element from sublists, and apply zero-based and negative indexing with slicing.

Master advanced list operations by replacing elements with index, slicing to create new lists, and concatenating, adding, or deleting items, while examining assignment effects.

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.

Explore how function arguments work in Python, using the round function to set one decimal place or the nearest whole number, and learn about optional arguments with examples.

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.

Explore how string methods work as specialized functions on string objects, including capitalize and upper, and learn how to call them with dot notation on strings.

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 Python list methods, including index to find the first occurrence, count to tally occurrences, remove to delete the first match, and reverse to invert the list in place.

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.