Python for Scientific Computing & Deep Learning (4 Projects)

Master conditional execution with the if statement, including keywords, conditions, and else branches. Use if, elif, and else to choose actions based on simple comparisons like x equals two.

The continue statement jumps to the next iteration of a loop, skipping the rest of the current iteration. An example shows output skipping five and moving from six to eight.

Learn how to use the try statement in Python to execute code safely, catch exceptions such as division by zero, and print informative error messages for robust error handling.

Explore using raise statements to trigger exceptions for invalid inputs, such as negative numbers, and raise a type error when the input type is not correct.

Explain the Python def statement, including function name, inputs, and a body, with a pass placeholder, and demonstrate a two number function that adds and prints the sum.

Explore yield statements that define generators, returning values while preserving local variables across calls. Continue execution from where they were left off, yielding 1, 2, and 3.

Explore the Python float type as floating point numbers with decimal points. See x = 1.2436 and test if a float is an integer using a built-in check; view hexadecimal representations.

Demonstrate bitwise operations on integers in Python, using X and Y to illustrate or, xor, and, shifts, and bitwise not, with binary representations and practical examples.

Explore the set type, an unordered collection of unique and immutable objects, with duplicates removed on declaration. Practice add, copy, clear, difference, intersection, and superset operations, including pop.

Explore frozenset type as an immutable version of a set, where elements stay the same after creation, convert a set to a frozenset using the frozenset keyword, adding not allowed.

Explore Python strings: declare and print strings with quotes, slice and concatenate, test membership, and apply methods like split, join, replace, partition, and case and whitespace checks.

Explore tuples as ordered, immutable collections that allow duplicates, access elements by indexing, and hold multiple data types, with length and type retrieval.

Learn mathematical operations in Python, including addition, subtraction, modulo, multiplication, exponentiation, division, logic expressions, and rounding.

Explore addition, subtraction, and modulo operations in Python on real numbers, and perform addition and subtraction on complex numbers with example variables.

Multiply and exponentiate real and complex numbers in python using the star and double-star operators; compute products, powers, and complex exponentiations, and print results.

Explore division in code within the Scientific Python & Deep Learning Masterclass, covering real and complex numbers and handling division by zero with a try and except.

Explore core Python logic expressions for branching, including membership tests with in and not in, and boolean operators and equality checks for building robust conditional logic.

Master rounding in Python by converting numbers to the nearest integer, including floor behavior and handling .5 cases, with examples 2.3 -> 2, 2.5 -> 2, and 2.7 -> 3.

Explore NumPy arrays, create and manipulate arrays using ones, zeros, empty, range, linspace, and shape, perform indexing, slicing, concatenation, and stacking, and visualize with matplotlib.

Sort NumPy arrays from smallest to largest using sort methods, then sort 2d arrays by rows or by columns by setting the second parameter to zero for column-wise sorting.

Learn to obtain an array's shape with the shape attribute. Reshape a 6x4 array into a 1x24, then into a 2x12, to see how new shapes emerge.

Learn to concatenate two-dimensional arrays using hstack and vstack to build horizontally and vertically stacked arrays. Explore practical examples of stacking arrays to combine data efficiently.

Perform basic mathematical operations on numpy arrays, such as sum, minimum, maximum, and scalar multiplication. Execute matrix operations: addition, subtraction, element-wise multiplication, dot method product, and transpose.

Save and load numpy arrays with save and load methods to a file, then reload them; use text file methods for 1d or 2d arrays, API format for higher dimensions.

Plot and customize with Matplotlib, using line plots with colored markers, axis limits, and labels. Create subplots and generate scatter plots from X and Y coordinates.

Explore optimization using SciPy to minimize multivariate functions, compare gradient-based methods with and without jacobians, and solve linear programming and root-finding problems.

Demonstrate single-variable integration with quad for a=3 and b=4 on 0 to 1, yielding 2.83879 with tiny error, and show a double integral of sqrt(1 - x^2 - y^2) over the unit disk with bounds y in -1 to 1 and x in -sqrt(1 - y^2) to sqrt(1 - y^2).

Create 3-D models with SketchUp and visualize them in Vista, then perform relative flux computations to analyze heat transfer and solar radiation via surface characteristics and mesh triangles.

Learn how wet proxies mask your IP and enable safe web scraping by routing requests through a proxy pool of multiple IPs with random permutations and user agents.

Build a real estate web scraper using a proxy pool, requests, logging, and Beautiful Soup to extract links, titles, prices, and coordinates across multiple pages.

Explore private variables and methods in object oriented programming by using double underscores. Access is restricted to the class and outside calls raise attribute errors.

Explore object oriented programming iterators by building a random number generator class. Instantiate the class and use its next method to yield random numbers between zero and one.