Data Science , Machine Learning : Ultimate Course Bootcamp

Begin with a complete Python introduction covering variables, data types, loops, conditionals, and functions, then explore object oriented programming including classes, objects, inheritance, polymorphism, abstraction, and encapsulation.

Explore Python as a high-level, object-oriented language, compare compiler and interpreter workflows, and survey Python applications from web development to data analysis, machine learning, and scientific computing.

Learn to install Python 3.11, configure path and environment variables, and set up development tools with PyCharm, VS Code, and Google Colab to run hello world.

Discover how to use Python variables as containers for data, assign values with the equals sign, understand automatic type inference, naming rules, case sensitivity, and print results.

Discover Python keywords, learn why reserved words cannot be variable names and that they are case sensitive, then list them in Google Colab with keyword.kwlist.

Explore Python data types, including integer, float, string, boolean, list, tuple, and dictionary, and learn typecasting and how to use type() to identify and convert types.

Explore how the Python id function reveals the memory address of a value, and verify that identical values share the same memory location across uppercase and lowercase variables.

Explore Python arithmetic operators including addition, subtraction, multiplication, division, floor division, and modulus with a=10 and b=20, noting Python is case sensitive and division versus remainder via modulus.

Explore Python logical operators and, or, and not using examples with a ten to twenty range to demonstrate true and false outcomes.

Explore Python comparison operators—equal, not equal, greater than, less than, greater than or equal, and less than or equal—using a=10 and b=20 with boolean outputs.

Explore bitwise operators in Python, including and, or, not, and xor, with a=2 and b=3 and the lcm method to derive bits, where zero means false and one means true.

Explore membership operators in Python, specifically in and not in, with list syntax. Understand how code checks if a value is present in the list and returns true or false.

Learn how Python identity operators use the id operator to check if values share the same memory address, with A, B, and C illustrating is and is not evaluations.

Explore conditional statements in Python, including if, elif, and else, through a mark-based grading example and printing grades or fail.

Master the Python for loop and range function to print the first ten values, understanding zero-based indexing, start and end points, and step values.

Learn to use the while loop in Python with a simple example that starts at i = 1 and runs while i < 10, printing hello world and incrementing i.

Explore break and continue statements in Python, demonstrate with a while loop, print and increment logic, and compare how break exits a loop versus continue skips a value.

Learn to create a Python function using def, name the function, define a=10, b=20, c=a+b, and call the function to print the result (30)—prepares for upcoming exceptions topic.

Learn to handle Python exceptions using try, except, and finally blocks, illustrated by a zero division error, capturing the exception and ensuring final statements run.

Create and print a string, identify its data type with type, get user input, inspect memory location, and explore string functions such as upper, capitalize, count, replace, slice, and len.

Explore how Python lists work as mutable data types, supporting homogeneous and heterogeneous elements, and learn to create, modify, insert, remove, extend, and sort with reverse.

Explore Python tuples as immutable sequences, contrast them with lists, learn tuple creation and slicing, and see how to convert a tuple to a list to modify values.

Learn how to create and manipulate dictionaries in Python, a mutable key-value data type, using curly braces, and leverage keys, values, items, and update to add or modify pairs.

Define a class and create an object in Python to illustrate object oriented programming. Instantiate the class, access attributes via the object, and print values.

Define a Python class and a method with def; compute c from a and b, print it, then instantiate the class and call the method with the object, noting self.

Explore Python inheritance, detailing single and multiple inheritance via father, son, and mother examples. Learn to define methods with self and access parent and child behavior.

Explore polymorphism in Python and its types, with emphasis on method overriding. See how a child class overrides a parent class method using single inheritance in a practical example.

Explore encapsulation in Python, wrapping data into a single unit and applying public, private, and protected access modifiers with class-based examples and practical use of self.

Explore abstraction in Python by building an abstract class with ABC, defining abstract methods, importing abstraction packages, and creating objects to illustrate hidden internals and visible interfaces.

Build a Python BMI calculator mini project using height and weight inputs, convert height to meters, and classify BMI as underweight, healthy, or overweight.

Learn why Python powers data science, master key libraries, numpy, pandas, matplotlib, seaborn, and plotly, for data analysis and visualization, and explore how data science enhances decision making and innovation.

Explore NumPy, a powerful open source library for scientific computing in Python, and learn how its continuous memory stores multi-dimensional arrays and matrices, making operations faster than Python lists.

Install numpy in Python and import numpy as np, using pip install numpy and the Google Colab syntax, then learn to import numpy for future array creation.

Explore numpy array types by creating zero-, one-, two-, and three-dimensional arrays with numpy's array constructor, printing values to illustrate the dimensional brackets concept.

Explore numpy datatypes and the dtype function to identify an array's data type, then demonstrate typecasting from integer to float and to string for practical projects.

Learn to use numpy's ndim function to determine an array's dimensions, illustrated with a three-dimensional array and a demonstration that prints the result.

Use the numpy arange function to create arrays with sequential values, such as 0 to 9 and 0 to 19. Learn zero-based indexing in arange.

Combine two one-dimensional arrays using numpy's concatenate function to create a new array. Create arrays [1,4] and [2,5], apply np.concatenate, and print the result to show the combined array.

Explore NumPy's ndmin function to create multidimensional arrays, converting a one-dimensional array into ten dimensions and inspecting the resulting shape.

Learn how to iterate numpy array elements one by one with the nditer function, using a for loop and conditionals to identify even and odd numbers.

Explore numpy functions, including copy to duplicate arrays, index-based value changes in 1d and 2d arrays, sorting with axis 0 or 1, and searching elements to locate their indices.

Install pandas in Google Colab, upload a csv or excel file, and read it with read_csv to access columns like duration, date, pulse, calories.

Explore pandas head and tail functions to view top or bottom values of a CSV file, with defaults to the top five and options to print 10 or 20 values.

Use the pandas info function to inspect a csv, revealing data types, five columns such as duration, date, pulse, and max pulse, null values, and memory usage for a dataframe.

Learn to use pandas dropna to remove rows with null values from a CSV dataset, reducing 33 rows to 30 and preparing for filling nulls.

Discover how to use Pandas fillna to replace null values with chosen values (e.g., 1000), the opposite of dropna, and see type-specific results for integers and floats.

Learn to add a title to a matplotlib graph using plt.title, applying it to the calories and pulse graph built from a csv file.

Explore the fundamentals of machine learning, including supervised and unsupervised approaches, with topics on regression, classification, clustering, and dimensionality reduction, plus real-world applications in health care, e-commerce, and finance.

Learn how the support vector machine finds the optimal hyperplane. Explore linear and non-linear SVM, max margin, and support vectors with applications in image recognition and text classification.

Explore how the k nearest neighbor algorithm classifies or regresses by selecting k, computing euclidean or manhattan distances, and predicting a label (cat or dog) by the minimum distance.

Explore k-means clustering, an unsupervised machine learning algorithm that groups similar data into distinct clusters using centroids, and apply the elbow method to determine the k value and wc ss.

Apply Naive Bayes, a supervised classification method, to predict outcomes using probability, building frequency and likelihood tables and applying Bayes' theorem to a weather dataset.

Explore how a decision tree classifier uses a root and leaf nodes to make yes/no predictions, guided by information gain, entropy, and Gini impurity.

Explore the random forest algorithm as an ensemble learning method that builds multiple decision trees via bagging to improve accuracy, handle outliers, and support classification and regression.