
Guard against prompt injection, jailbreaking, hallucination, bias, and privacy by building layered defenses from the start, separating trusted instructions from untrusted data, grounding answers, and red-teaming continuously.
Master llm ops by designing, testing, and deploying prompts with version control, cost and latency management, and robust monitoring. Route tasks to the right models and guard against drift.
Discover why Python is favored by big companies, and follow a two-step path—certification courses and online Python contests—while exploring six features, cross-platform use, and data analysis and artificial intelligence applications.
#Set up environment google colab:
Colab is a python development environment that runs in the browser using google colab. In shortly, it allows you to write and execute python in your browser. The document you are reading is not static, but an interactive environment called a colab notebook that lets you write and execute code. It is a virtual machine you can access directly. To runs commands at the VM’s terminal, prefix the line with an exclamation mark.
#data type and variables, keywords:
A value is a basic thing in program works with letters or number. These values are assigned with variables. They belong to different types such as integer and “ hello” is a string because it contains letters. Variable is a capability of storing some kind of data. It then can be modified, stored or displayed. Variables are basically referred as containers that hold information. Keywords are pre-defined, reserved words which has special meaning. They are a part of syntax.
#Take inputs:
Input can come in various ways, for example from a database, another computer, mouse clicks and movements or from the internet. For this purpose, python provides the function named input(). There are two functions raw_input() which reads an input and returns a string and other one is input() which reads it and return the python type.
#print statement:
The print() function prints the specified message to the screen, or other standard output devices. The message can be a string, or any other object, the object can be converted into a string before written to the screen. Sometimes it may happen you need to print blank line in your python program for that “\n” is used.
#Intro to lists, tuples and dictionaries:
The Lists are very similar to arrays. It is a sequence of values. The values in list are called elements or sometimes items. List is created by placing all items inside a square bracket. The Tuples is similar structure as list. In tuples we cannot change the elements after assigning. Tuples are used for grouping data and enclosed in parenthesis. Dictionary deals with an unordered set of key and value pairs. It contains data in curly braces. The pair is known as item and are separated by colon.
#string operations:
String in python are a set of characters represented in the form of single quotation mark or double quotation marks. There are various string operations such as capitalize() which represents first letter of string. Then lower() which converts string into lowercase. Then many more like index(str,beg=0,end), find(), len(string).
#Operators:
Operators are special symbols that represent computations like addition and multiplication. The values the operator uses are called operands. When a variable name appears in the place of an operand, it is replaced with its value before the operation is performed.
#list operations:
List are basically the sequence of values. The values in list are called the items or elements. List operations consist of append method which basically add or append the object to the list. Then there are many such methods such as index which returns the lowest index in list that object appears and many more such operations.
#tuple operations:
Tuples are sequences just like list. The main difference is it cannot be changed unlike list. Tuples operations contains concatenation which refers to combining contents of two tuples using + sign. The result will be a single tuple. Other is in operator which checks the element is the tuple. And many more like iteration and so on.
#set operations:
Set is an unordered collection of items. Every set element is unique and cannot be changed i.e immutable. Set can be used to carry out mathematical set operations like union, intersection, difference. We can do this with methods.
#dictionary operations:
Each key is separated from its value by colon, the items are separated by commas and the whole thing is enclosed in curly braces. The dictionary contains clear method which refers to removing all the elements from dict. There are many such methods copy, get, items and so on.
#datatype conversion:
Value is one thing works with a letter or number. This values are assigned with variables. There are two types integer and string. Type conversion is converting one type of data to another type. Which is also known as type casting.
#importance of indentation:
Indentation refers to spaces of the beginning of the code line. In python indentation is very important to indicate a block of code.
#random number and range function:
There is need to generate random numbers when studying a program for different range of values. Python basically generate such random numbers by using the random module. The range function return a sequence of numbers starting from 0 by default and increment by 1 and stops before specify number. It has parameter such as start, stop, step.
#sequencial, selection and repetition:
Sequential execution is when statements are executed one after another in the order. Selection used for decisions, branching-choosing between two or more alternative paths. Repetition is used for looping that is repeating a piece of code multiple times in a row.
#math library:
Library basically describes a collection of many modules. The math library provides us access to some common math functions and constants, which can be use throughout the code for more complex mathematical computations.
#datetime and calendar module:
A date is not a datatype of its own, but we can import module named datetime to work with dates as date object. This module has many methods to return information about the date object. Methods such as strftime(). Calendar module is an in-built module which handles operations related to calendar. It has methods such as itermonthdates(), yeardatescalendar() etc.
# create, edit, write, read text:
Files are persistent because the run for a long time, they keep some data in permanent storage. Opening a file for writing uses the built-in function that is open(). This returns a file object. Write function takes one parameter, which is the string to be written. Read function for getting the characters read in the file. Edit will simply make some editing in your file.
#exception handling:
Exception is a condition in a program code. It is basically an error which happens during the execution of the program. It must be either handled immediately otherwise it terminates or quits. This can be handle by placing code in try block. After try include except.
#Collection Module-
Collection are basically type of containers that are used to store collections of data for example list, dict, tuples etc. These are built-in collections. It was introduced to improve the functionalities of the built-in collection containers. There are so many functions in this module like counter which takes an iterable. The other is element() which gets the items of a counter object. And many such funtions.
#RegularExpression-
The regular expression can be defined as a specific type of text pattern. These patterns you can use with many applications and programming languages. By using this you can verify whether the inputs fit into the text pattern, to replace text matching the pattern, to find text. The re module raises re.error if error occurs and many such things.
#list comprehension, fronzen set function-
It is a way to define or create a list. These lists have often qualities of sets, but are not in all cases sets. It is a complete substitute of lambda function and map, filter and reduce function. The frozen set is an inbuilt function. It takes an iterable object as input and makes them immutable that is unchangeable.
#CSV File Operations-
A CSV file(comma separated value file) is a type of plain text file that uses specific structuring to arrange tabular forms of data. Because it’s a plain text file so it only contains actual text data. If you want to read data then reader function is used. This function takes each row of the file and make a list of all columns. Then you have to choose column you want variable data for. For writing you need to use writer function.
#user define functions and in-built functions-
A function that you define yourself in program is basically a user-defined functions. You can use any name to a user-defined functions. We can’t take the keyword as python. For defining this function we use def as a keyword. Built-in functions are basically provided by the language itself that is it’s a part of the high level language which can be executed by a simple reference with specifying the arguments.
# concept of recursion and iterator-
Recursion is a common mathematical and programming concept. It means that a function calls itself. This has the benefit of meaning that you can loop through data to reach a result. Iterator is simply a object that can be iterated upon or contains a countable number of values. In technically, its an object which implements the iterator protocol, which consist of methods __iter__() and __next__().
#generator and decorators-
Generators are used to create iterators, but with different approach. Generators are simple function which returns an iterable set of items, one at a time. This function can generate as many values as it wants. Decorators are very powerful tool since it allows programmers to modify the behavior of function or class. In this functions are taken as the argument into another function and then called inside the wrapper function.
#global and local variables-
A variable which is declared outside of the function is known as global variable. It means that this variable can be accessed inside or outside of the function. A local variable is declared within the function or is an argument passed to a function. So it states that this function can be used only within that function itself.
#logging module-
This module is in the standard library that provides a flexible framework for emitting log messages from programs. With the logging module imported you can use something called “logger” to log messages that you want to see. There include some standard levels such as debug, warning, error, info and critical.
#lambda, map, filter and reduce-
Lambda function is a single line function declared with no name, which can have any number of arguments, but it has only one expression. Map function returns a map object(which is iterator) of the results after applying the given function to each item of a given iterable(list, tuple etc ). Filter method filters the given sequence with help of function that tests each elements in that sequence. Reduce function is used to apply a particular function passed in its arguments to all the list elements mentioned in the sequence.
#python queue-
Like stack, queue is a linear data structure that stores items in first in first out manner. With a queue the least recently added item is removed first. For example consumers for a resource where the consumer that came first is served first. Operation includes rear, front, dequeue, enqueue. It can be implemented by list, collections.deque and queue.queue.
#isinstance, use of format, timeit()-
The isinstance function returns true if the specified object is of the specified type, otherwise false. For example if the type parameter is a tuple, this function will return true if the object is one of the types in the tuple. The format method formats the specified values and insert them inside the string placeholder. the format method returns the formatted string. Timeit module provides a simple way to find the execution time of small bits of python code.it has both a command-line interface and a callable one.
#zip function-
This function returns a zip object, which is an iterator of tuples where the first item in each passed iterator is paired together, and then the second item in each passed iterator are paired together etc. If the passed iterators have different lengths, then the iterator with the least items decides the length of new iterator.
Explore eval, exec, and repr in Python. Learn how eval evaluates expressions to integers, how exec runs dynamic code, and how repr shows printable representations of objects.
Explore how Python switch case controls program flow using a dictionary mapping and a day-of-week function, and build a simple calculator with plus, minus, multiply, and divide operations.
Learn how Python's ternary operator uses a conditional expression to choose outcomes in one line. See multiple syntax forms, compare minimum and maximum values, and print results directly.
#OOP-
Python is such a programming language which supports different approaches. One of the popular approach is by creating objects. This is known as object -oriented programming (OOP). It focuses on creating reusable code which is known as DRY (don’t repeat yourself). It includes class, objects and so on.
Learn Python basics in a crash course covering data types, input/output, formatting, control flow, functions, and key modules for machine learning and data science.
Explore NumPy basics: create and reshape arrays, understand zero indexing and negative indexing, slice and index multi-dimensional data, and study fundamental 3-D structures for efficient data access.
Explore NumPy fundamentals by importing NumPy, creating ranges with arange and linspace, generating random data, shaping arrays, performing slicing, sorting, normalization, and counting occurrences for data analysis.
Explore numpy for numerical operations, covering element-wise arithmetic, rounding, floor and ceiling, powers, remainders, absolute value, inverse, and basic matrix operations.
Explore practical string operations in python, including capitalization variants, length and counting, and equality checks. Learn to replace, split and join strings, and apply a ham/spam text classification example.
Explore NumPy for linear algebra and statistics, computing determinants, means, medians, variances, standard deviations, covariances, and inverses of matrices, plus diagonal matrix operations.
Explore how NumPy date and time functions handle numeric dates, calculate yesterday and tomorrow, determine days in February during leap years, and identify Sundays and weeks.
Explore NumPy logical operations in a tutorial that demonstrates element-wise and, or between arrays, with value comparisons from zero to five to generate truth-based results.
#numpy official site-
There are some websites which you can refer for numpy. For example numpy.org. So for easy learning there is a QuickStart which resides in this org website only. Its too useful and explanation is up to the mark.
Learn to work with pandas series, created from lists and dictionaries, and perform elementwise operations, comparisons, sorting, and standard deviation while exploring indices, data types, and basic statistics.
Learn pandas basics by creating series from lists and dictionaries, labeling indices, and converting arrays. Build and explore data frames to organize website data for analysis.
Explore merging, joining, and concatenating dataframes with pandas, using keys to align data and perform left and right joins, while previewing index handling and concatenation results.
Explore how pandas group by operation computes total sales by company and reveals mean, standard deviation, counts, and descriptive statistics, including data frame transposition.
Explore handling missing data in pandas by creating a dataframe with A, B, and C, identifying gaps, dropping incomplete rows, and filling missing values with random values or mean imputation.
Explore how pandas pivot tables transform Excel-like regional data into a multidimensional summary of managers, software engineers, projects, hours, and profit, including mean, maximum, and minimum insights.
Explore excel file operations with pandas by reading an excel file, inspecting head and tail, checking nulls and data types, and computing sum, mean, min, and max.
Explore working with different time frequencies in Pandas to build data frames with date ranges and six-month periods. Ignore deprecation warnings and format dates to create month-based and date-time frames.
Learn to create and apply a boolean index in pandas, convert a dictionary to a DataFrame, filter data by conditions, and inspect index slices for efficient data selection.
#scipy tutorial-
It is a scientific library for python is an open source, BSD-licensed library for mathematics, science and engineering. This library is depended on NumPy, which provides convenient and fast array manipulation. The main reason of this library is to work with NumPy arrays. It provides many numerical practices for integration and optimization.
#scipy official site-
In this video, you will see which websites you need to use and get used to it. For example scipy.org. This website is too useful and you can learn many things from that.
Explore Matplotlib essentials for visualizing data with bar charts, histograms, scatter plots, line plots, box plots, and subplots, plus image processing and adding text and equations to plots.
Learn to plot with matplotlib by creating line and square-function visuals, experimenting with subplots, and generating histograms and box plots from a 1998 multi-country dataset.
Learn to plot with Matplotlib, generating simple plots, scatter plots, histograms, bar charts, and pie charts, and create subplots to compare data.
Create and customize visualizations with Matplotlib, including bar charts in multiple orientations, with labeled axes, titles, and color options, using randomly generated student data.
Explore Matplotlib by viewing its official site, selecting the right plot (including scatter plots) for your data, and learning to adapt example code in Jupyter Notebook or Colab.
Explore Seaborn for Python data visualization by loading built-in datasets from Seaborn's data center, inspecting with head, tail, sample, and describe, and practicing with Titanic examples.
Explore seaborn by visualizing the tips dataset with distribution plots, joint plots, hex plots, kde and regression lines, box and bar charts, count plots, heatmaps, and pivot-style analyses.
Explore Seaborn for Python data visualization, view the official gallery, copy-paste code into a Jupyter notebook or Google Colab, and generate plots like heat maps.
Explore Plotly in Python for visualization, install in Google Colab, create line, bar, and scatter plots with Plotly Express, and visualize iris dataset with charts like parallel coordinates and sunburst.
Unlock the power of Generative AI by learning how to communicate effectively with modern AI models.
This comprehensive course takes you beyond basic ChatGPT prompting and teaches you how to design, test, evaluate, and optimize prompts for real-world AI applications.
You will start with the foundations of Prompt Engineering and progressively move into advanced techniques including reasoning models, thinking controls, context engineering, structured outputs, tool use, agentic prompting, evaluation, optimization, safety, and LLMOps.
The course is designed to help you understand not only WHAT prompt to write, but WHY a prompt works, how to improve it, and how to build reliable AI workflows around Large Language Models (LLMs).
What you will learn
• Fundamentals of Prompt Engineering
• Core prompting techniques and best practices
• Designing effective instructions and prompts
• Reasoning models and thinking controls
• Prompt Engineering across different AI models
• Context Engineering for better AI responses
• Structured outputs and reliable AI responses
• Tool use and function-oriented prompting
• Agentic Prompting and AI agent workflows
• Practical AI applications and use cases
• Prompt evaluation, testing and optimization
• Risks, safety, limitations and AI guardrails
• Production considerations and LLMOps
The course also includes a bonus Python session covering Python fundamentals, NumPy, Pandas, SciPy, Matplotlib, Seaborn and Plotly to help learners who want to work more practically with AI and data.
Who is this course for?
This course is suitable for:
• Students who want to build practical Generative AI skills
• Developers and software professionals working with AI/LLMs
• Data science and analytics professionals
• AI/ML enthusiasts
• Business professionals looking to use AI more effectively
• Entrepreneurs and professionals building AI-powered solutions
• Anyone who wants to move beyond basic ChatGPT usage and understand professional Prompt Engineering
You do not need to be an AI researcher to get started. The course gradually progresses from fundamental concepts to advanced Prompt Engineering and AI application techniques.
What makes this course different?
Instead of treating prompting as simply "asking ChatGPT better questions", this course approaches Prompt Engineering as an engineering discipline.
You will learn how prompts interact with models, context, reasoning, tools, structured outputs, evaluation and AI agents.
By the end of the course, you will have a structured framework for designing better prompts and building more reliable AI-powered workflows.
Start your journey from basic prompting to advanced Prompt Engineering and learn how to work effectively with modern AI systems.