Master Numpy Foundation and Practice Challenging Exercises

Install Anaconda by downloading the individual edition from anaconda.com, agreeing to the license, and choosing the install location. Then launch Navigator to access Jupiter and Spyder tools.

Learn how to change the default Jupyter notebook startup folder in Anaconda Navigator to a chosen working directory for Numpy foundation practice.

Explore Python as a versatile, easy-to-learn, platform-independent language for data science, with emphasis on libraries like numpy, pandas, and scikit-learn, and using Jupyter Notebook for interactive prototyping.

Explore the scope of the jupyter notebook in this course and how ipynb files serve as the code format, enabling code execution with narrative text and equations.

Explore the Jupiter notebook as an interactive data science environment that executes code blocks (cells), shows outputs and charts, and helps explore computations and machine learning results.

Master Jupyter notebook part 2 shortcuts for saving, switching between command and edit modes, and executing cells with ctrl, plus tab completion for Python functions.

Explore the Jupiter notebook section, learn code and markdown cells, convert between modes, and use shortcuts to insert, delete, and format cells with headings, lists, italics, and other markdown features.

Practice printing text and intermediate outputs in a jupyter notebook, and time code with %%time and %time while using exclamation marks to run shell commands and install packages.

Explore how the Jupiter notebook kernel, humorously called colonel, executes Python code, manages memory, and responds to restart, interrupt, and status commands like run all and run all below.

See how Python automatically assigns a type to variables when you declare integers or strings, and how typecasting converts values with int() while preserving the original variable until reassigned.

Master NumPy and practice exercises by mastering Python operators, including comparison operations like equal to and greater than, and logical operations with and, or, not and the boolean data type.

Learn how to use if statements in Python, including indentation, blocks, elif and else, and logical operators to control print statements based on variable conditions.

Master Python string basics, from quoting and measuring length with len to concatenation and type conversion with str. Learn upper, lower, capitalize, strip, replace, and split to manipulate text.

Master string indexing and slicing with positive and negative indices, start, end, and step. Use find, in, and equals to locate patterns and compare values, also handle case sensitivity.

Explore Python lists as multi-item collections, learning list creation, accessing elements by index, slicing with start, end, and step, and sorting or reversing lists with in-place changes.

Build on list operations by updating and modifying elements with indexing and slicing, inserting and removing items, and handling in-place changes and first-occurrence removals.

Explore how for loops replace repetitive code by iterating over lists with range and len, and use enumerate to access index and value.

Master loops and list comprehension in Python by exploring while loops, break and continue, and converting lists into squares with range and conditional filtering for even numbers.

Explore dictionaries as mutable key–value collections, learning to create, access by key, modify or add values, and delete entries. Practice iteration with items, keys, and values, plus dictionary comprehension.

Explore Python sets, an unordered collection that stores unique values and supports operations like add, remove, membership tests, and set theory operations such as union, intersection, difference, and symmetric difference.

Explore Python tuples, an immutable collection similar to lists, learn indexing and slicing, see how tuples can serve as dictionary keys, and create dictionaries with tuple keys via comprehension.

Define and use functions in Python to reduce code repetition, pass data, and return results, covering default and keyword arguments, by-reference mechanisms, and anonymous functions.

Explore how Python treats function arguments with immutable types like integers and strings, and mutable types like lists and dictionaries, showing how reassignment vs in-place modification affect the calling function.

Explore default arguments, keyword arguments, and anonymous lambda functions, and learn how positional and keyword calls override defaults and control behavior.

Explore Numpy as a fast, multidimensional array library that stores homogeneous data in contiguous memory, enabling vectorized operations and performance gains over Python lists for machine learning tasks.