Python for Beginners: A Gateway to Machine Learning

Learn to convert strings to upper or lower case in Python using the upper and lower methods, and to concatenate strings with the plus operator, including inserting spaces.

Explore how to use escape characters in Python to include quotes and control string formatting, using backslashes for new lines, tabs, and other escapes like \n, \t, and \r.

Learn how to use the set collection object in Python, including its duplicate-free behavior, adding elements with add, removing with pop, and checking type.

Explore Python dictionaries as key-value stores, learn to access values with get, remove pairs with pop, and check length to manage referential data efficiently.

Explore how the for loop in Python enables iterative tasks by iterating over a list of names and printing each friend.

Understand how the while loop runs until a condition is met, using a counter starting at zero, incrementing to print 0 through 4, and including an else clause after completion.

Discover how to define and reuse functions in Python using def, with three-argument calls and conditional logic for plus, minus, and multiply operations, demonstrated in PyCharm.

Explore how decorators in Python modify or extend function behavior, using a logger wrapper to log details and then call the original function.

Explore how a child class inherits attributes and methods from a parent class in Python. The example with the human and African human demonstrates a constructor and the speak method.

Explore how a child class inherits from multiple parents in Python, using a human and a doctor as parent classes, and learn how to initialize both constructors and pass expertise.

Define a Python module by saving code in a .py file and import it into other programs. Use PyCharm to create mymodule.py and access its objects with Mymodule.run.

Examine the basics of machine learning within artificial intelligence. See how input plus sample output guides predictions, data prep, and 80% training 20% validation with scikit learn.

Explore essential python libraries for machine learning such as NumPy, pandas data frames, matplotlib, scikit-learn, TensorFlow, and PyTorch, and learn to develop with Jupyter notebooks via Anaconda.

Analyze daily attendance data, replace alphabets with numbers to enable machine learning, and split 80/20 for training and testing using pandas and scikit-learn components.

Learn to build a simple machine learning model using sklearn classification, fit and predict, and evaluate with mean absolute error and root mean square error to achieve about 67% accuracy.