AI ML Interview Introduction

Durgesh Sharma (AI & DS ATS).pdf

PDF

tommorrow i have my interview on my ai and ml resume profile so can you help me with an
introduction of mine

Here’s a personalized self-introduction based on your AI & ML resume that you can use during your
interview. It’s structured to be confident, professional, and within the 90–120 second range:

Self-Introduction for Interview (AI & ML Profile):

"Good morning, and thank you for the opportunity.
I’m Durgesh Kanhaiyalal Sharma, a final-year engineering student specializing in Artificial
Intelligence and Data Science from Pune Vidyarthi Griha’s College of Engineering in Nashik.
Over the past year, I’ve gained hands-on experience through internships and projects that focused on
real-world AI applications. At Infosys, I contributed as an AI Engineer, building NLP-driven solutions to
optimize enterprise decision-making. Prior to that, I worked as a Data Analyst at Apna Guide, where I
handled large datasets and built data pipelines and dashboards. I also have backend and full-stack
exposure from earlier roles at Toku Tech Solutions and Xmega, which helped strengthen my
understanding of scalable systems.
In terms of technical skills, I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face for AI/ML,
with strong foundations in BERT, GPT, and traditional NLP tools like NLTK and SpaCy. I’ve applied these
skills in projects like a Context-Aware NLP system for geospatial queries, an AI-powered call center
assistant using LangChain, and a sentiment analysis tool trained on large datasets.
Beyond my technical capabilities, I’m a strong team player with leadership and problem-solving skills.
I’m passionate about using Generative AI and NLP to build impactful, scalable products, and I’m excited
to contribute to innovative solutions in your organization."

✅ Tips:
Customize the last paragraph to mention the company or role specifically.

Keep your tone enthusiastic and clear.

Practice it 2–3 times to ensure a natural flow.

If you'd like a shorter version, a Hindi-English mixed version, or one tailored to a specific job
description or company, let me know and I’ll adjust it accordingly.
Would you also like a PDF of this version to keep handy?
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 now give most commonly used numpy, pandas and matplotlib methods along with few arguments

Here’s a practical list of most commonly used methods/functions in NumPy, Pandas, and Matplotlib,
along with their key arguments and short explanations. All references are sourced from official
documentation and widely accepted usage practices.

📌 NumPy (Numerical Python)

📘 Source: NumPy Official Docs
Method Description Key Arguments
np.array() Create an array object, dtype, ndmin
np.zeros() Create an array filled with zeros shape, dtype
np.ones() Create an array filled with ones shape, dtype
np.arange() Range of values start, stop, step, dtype
np.linspace() Evenly spaced numbers start, stop, num, dtype
np.reshape() Reshape array newshape, order
np.concatenate() Join arrays arrays, axis
np.mean() Mean of elements a, axis, dtype
np.std() Standard deviation a, axis, dtype

np.dot() Dot product of 2 arrays a, b

np.random.rand() Random values (uniform dist.) shape(s) as args
np.random.randn() Random values (normal dist.) shape(s) as args

📌 Pandas (Data Manipulation)

📘 Source: Pandas Official Docs
Method Description Key Arguments
pd.read_csv() Read a CSV file filepath_or_buffer, sep, header, usecols
df.head() First N rows n (default = 5)

df.tail() Last N rows n

df.info() Summary of DataFrame —

df.describe() Stats summary percentiles, include, exclude

df.isnull() Detect missing values —

df.fillna() Fill NA values value, method, inplace

df.dropna() Remove NA rows/cols axis, how, inplace

df.groupby() Group data by, axis, as_index

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 Method Description Key Arguments

df.merge() Merge DataFrames right, how, on, left_on, right_on

df.sort_values() Sort rows by, ascending, inplace

df.apply() Apply function func, axis, raw

df.loc[] Access by label row/column labels

df.iloc[] Access by position row/column indices

📌 Matplotlib (Visualization)
📘 Source: Matplotlib Official Docs
Method Description Key Arguments
plt.plot() Basic line plot x, y, label, color, linestyle

plt.scatter() Scatter plot x, y, color, s, alpha

plt.bar() Bar chart x, height, color, width

plt.hist() Histogram x, bins, color, alpha

plt.xlabel() Label x-axis xlabel, fontsize

plt.ylabel() Label y-axis ylabel, fontsize

plt.title() Add title label, fontsize, loc

plt.legend() Show legend loc, fontsize

plt.grid() Add grid which, axis, color, linestyle

plt.show() Render the plot —

⚠️ Cautions & Best Practices

Always check for missing data before applying transformations (especially in Pandas).

Use np.set_printoptions(precision=n) or pd.options.display to control output display.

Visualizations should include labels, legends, and grid for clarity.

Avoid chained assignments in Pandas (df[col].fillna(...)) without using .copy() or inplace=True.

Would you like code examples for these, or a cheatsheet-style PDF for quick revision?

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 now for list and dictionary methods

Absolutely, Durgesh! Here's a concise and practical guide to the most commonly used Python list and
dictionary methods, including their key arguments, behavior, and important cautions — all based
on the official Python documentation.

🟩 List Methods in Python

📘 Docs: Built-in Types – list
Method Description Key Arguments
list.append(x) Adds element x at the end x (any type)

list.extend(iterable) Adds multiple items iterable (list, tuple, etc.)

list.insert(i, x) Inserts x at index i i (int), x

list.remove(x) Removes first occurrence of x x (must exist)

list.pop([i]) Removes & returns item at index i (default last) i (optional)

list.clear() Removes all items —

list.index(x) Returns first index of x x, optional start, end

list.count(x) Counts occurrences of x x

list.sort() Sorts list in-place key, reverse

list.reverse() Reverses list in-place —

list.copy() Returns a shallow copy —

✅ Example:
python

a = [1, 2, 3]
a.append(4) # [1, 2, 3, 4]
a.pop(0) # [2, 3, 4]

⚠️ Caution:
remove() throws ValueError if element not found.

sort() modifies the list; use sorted(list) for a copy.

🟨 Dictionary Methods in Python

📘 Docs: Built-in Types – dict
Method Description Key Arguments
dict.get(key[, default]) Returns value, or default if key missing key, default

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 Method Description Key Arguments
dict.keys() Returns view of keys —

dict.values() Returns view of values —

dict.items() Returns key-value pairs as tuples —

dict.update([other]) Adds or updates from another dict other (dict or iterable)

dict.pop(key[, default]) Removes key and returns value key, optional default
dict.popitem() Removes and returns last item (LIFO) —

dict.setdefault(key[, default]) Gets value or sets default if key missing key, default
dict.clear() Removes all items —

dict.copy() Returns shallow copy —

key in dict Check key existence —
✅ Example:
python

d = {'a': 1, 'b': 2}
d.get('c', 0) # Returns 0
d.update({'d': 4}) # {'a':1, 'b':2, 'd':4}

⚠️ Caution:
pop() without default will raise KeyError if key is missing.

popitem() removes items in insertion order (Python 3.7+).

✅ Would You Like:

Code-based practice questions for these methods?

A PDF cheat sheet for offline reference?

Real-world interview questions that test these?

Let me know what you prefer, and I’ll get it ready right away.

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 is there any thing im forgetting in basics

Great question, Durgesh — reviewing core Python basics before an AI/ML interview is smart. Here's a
comprehensive checklist of essential Python concepts you should know — especially those
commonly evaluated in interviews for AI/ML/Data Science roles.

✅ 1. Python Basics (Syntax and Semantics)

Topic What to Review

✅ Variables & Data Types int, float, str, bool, list, tuple, dict, set

✅ Type Conversion int(), str(), float()

✅ Operators Arithmetic, comparison, logical, identity, membership

✅ String Methods split(), join(), replace(), strip(), find()

✅ Conditional Statements if, elif, else, ternary expression

✅ Loops for, while, break, continue, range()

📘 Source: Python Data Types

✅ 2. Functions and Scope
Topic What to Know

✅ Function Definitions def, arguments, *args, **kwargs

✅ Lambda Functions lambda x: x + 1

✅ Scope Rules LEGB (Local → Enclosed → Global → Built-in)

✅ Decorators & Closures Basics of wrapping functions

📘 Source: Defining Functions

✅ 3. Data Structures
Type Key Features

✅ List Mutable, ordered, supports slicing

✅ Tuple Immutable, used for fixed data

✅ Set Unordered, no duplicates

✅ Dictionary Key-value pairs, mutable

✅ Comprehensions List/Dict/Set comprehensions — [...], {k: v}, {...}

📘 Source: Data Structures

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✅ 4. Exception Handling
Concept What to Know
✅ Try-Except Handle exceptions

✅ Finally & Else Cleanup code

✅ Custom Exceptions Use raise and define Exception subclasses if needed

📘 Source: Errors and Exceptions

✅ 5. File Handling
Function Purpose
open() Opens a file

.read(), .readlines(), .write() File operations

with open(...) as f: Context manager (safe file handling)

📘 Source: File I/O

✅ 6. Modules & Packages
Concept Example

Importing import numpy as np, from math import sqrt

Custom Modules Writing your own .py files

__name__ == '__main__' For script execution logic

📘 Source: Modules
✅ 7. Object-Oriented Programming (OOP)
Concept Key Items
✅ Class & Object class MyClass:

✅ __init__ constructor Object instantiation

✅ Inheritance class Child(Parent)

✅ Polymorphism, Encapsulation Method overriding, private variables

✅ @classmethod, @staticmethod Alternate constructors, stateless methods

📘 Source: Classes
✅ 8. Iterators, Generators & Comprehensions
Concept What to Know

✅ Iterators __iter__, __next__

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 Concept What to Know

✅ Generators yield keyword

✅ Generator Expressions (x for x in range(5))

✅ List Comprehension [x*x for x in range(5)]

📘 Source: Iterators
✅ 9. NumPy, Pandas, Matplotlib Basics
You already covered this — just make sure you’re also familiar with:

Broadcasting in NumPy
DataFrame filtering & indexing in Pandas

Subplots and multiple plots in Matplotlib

✅ 10. Python for AI/ML

Tool Key Concepts
Scikit-learn Model selection, train_test_split, fit/predict

Matplotlib/Seaborn Visualization best practices

NumPy Efficient matrix ops

Pandas Data preprocessing

Jupyter Notebook Code + explanations

Virtual Environments venv, conda environments for reproducibility

🛡️ Caution Points:
Avoid using mutable default arguments (def func(x=[]))
Be careful with copy vs reference in lists/dictionaries (copy.deepcopy)

Always test for key existence in dicts before access (dict.get() or in)

Would you like this checklist as a downloadable PDF, or a quick flashcard quiz version to practice?

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 give me eg of class ,object ,super keyword, inheritence , poly morphism both, types and one eg of
excetion handling

Perfect, Durgesh! Here’s a compact but thorough explanation with examples of:

class, object

inheritance with super()

Polymorphism (compile-time and run-time)
Exception handling

All examples are beginner-friendly and interview-appropriate, with behavior consistent with the Python
official documentation.

🧱 1. Class and Object Example

python

class Car:
def __init__(self, brand, year):
self.brand = brand
self.year = year

def display_info(self):
print(f"{self.brand} - {self.year}")

# Object creation
car1 = Car("Toyota", 2020)
car1.display_info() # Output: Toyota - 2020

🧬 2. Inheritance with super() Keyword

python

class Vehicle:
def __init__(self, brand):
self.brand = brand

def start(self):
print(f"{self.brand} is starting...")

class Bike(Vehicle):
def __init__(self, brand, cc):
super().__init__(brand) # calls parent class constructor
self.cc = cc

def show(self):
print(f"Brand: {self.brand}, CC: {self.cc}")

bike1 = Bike("Yamaha", 150)

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 bike1.start() # Output: Yamaha is starting...
bike1.show() # Output: Brand: Yamaha, CC: 150

🔄 3. Polymorphism
(A) Compile-Time Polymorphism (Method Overloading - simulated)

Python does not support true method overloading, but you can simulate it using default arguments or
*args:

python

class Calculator:
def add(self, a, b=0, c=0):
return a + b + c

calc = Calculator()
print(calc.add(5)) # Output: 5
print(calc.add(5, 10)) # Output: 15
print(calc.add(5, 10, 15))# Output: 30

(B) Run-Time Polymorphism (Method Overriding)

python

class Animal:
def sound(self):
print("Some generic sound")

class Dog(Animal):
def sound(self):
print("Bark")

class Cat(Animal):
def sound(self):
print("Meow")

def make_sound(animal):
animal.sound()

make_sound(Dog()) # Output: Bark

make_sound(Cat()) # Output: Meow

🛡️ 4. Exception Handling Example

python

try:
num = int(input("Enter a number: "))
result = 10 / num

print("❗
except ZeroDivisionError:
You cannot divide by zero.")

print("
else:
❗
except ValueError:
Please enter a valid number.")

print(f"Result is {result}")

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 finally:
print(" ✅ This block always executes.")

✅ Summary Table
Concept Key Point
class Blueprint for creating objects

object Instance of a class

super() Access parent class methods
Inheritance Child class inherits parent

Polymorphism Same method, different behavior

Exception Handling try, except, else, finally

Would you like these in a PDF cheat sheet, or with practice questions for mock interview prep?

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 also explain me data abstraction and few more leftover concepts like a lambda eg, and set methods

Great follow-up, Durgesh! Let's now cover the remaining core OOP and functional programming
concepts you're likely to be asked in interviews, including:

✅ Data Abstraction
✅ Lambda functions
✅ Set methods
✅ A few more leftover important topics (e.g., enumerate, zip, ternary ops, map/filter/reduce)

All examples are clean, interview-relevant, and aligned with official Python docs.

🧱 1. Data Abstraction
Abstraction means hiding complex implementation details and exposing only the necessary parts of an
object.

✅ Python doesn't have true private access modifiers, but uses naming conventions:
python

from abc import ABC, abstractmethod

# Abstract Base Class

class Shape(ABC):
@abstractmethod
def area(self):
pass

class Circle(Shape):
def __init__(self, radius):
self.__radius = radius # __radius is private

def area(self):
return 3.14 * self.__radius ** 2

c = Circle(5)
print(c.area())
# print(c.__radius) # ❌
# Output: 78.5
Raises AttributeError

📌 Key Concepts:
ABC and @abstractmethod enforce abstraction.
Double underscore __ makes an attribute private (name-mangled).

⚡ 2. Lambda Functions
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Lambda is an anonymous function (used for short, throwaway operations).

python

# Normal function
def square(x):
return x * x

# Lambda version
square = lambda x: x * x
print(square(4)) # Output: 16

✅ Common with map(), filter():

python

nums = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x*x, nums)) # [1, 4, 9, 16, 25]
evens = list(filter(lambda x: x % 2 == 0, nums)) # [2, 4]

📘 Lambda Docs
🔢 3. Set Methods
Sets are unordered collections with no duplicate values.

python

a = {1, 2, 3}
b = {3, 4, 5}

print(a.union(b)) # {1, 2, 3, 4, 5}
print(a.intersection(b)) # {3}
print(a.difference(b)) # {1, 2}
print(a.symmetric_difference(b)) # {1, 2, 4, 5}

a.add(6) # Adds an item

a.discard(2) # Removes item if present
a.clear() # Removes all items

📘 Set Methods
🧩 4. Bonus: Other Crucial Python Concepts
Concept Example Purpose
enumerate() for i, val in enumerate(['a', 'b']): Index + value
zip() zip([1,2], ['a','b']) → [(1, 'a'), (2, 'b')] Parallel iteration

Ternary Operator x = 10 if a > b else 5 One-line if-else

all() / any() all([True, True]) Check if all/any elements are true
map() map(lambda x: x*2, lst) Apply function to each item

filter() filter(lambda x: x%2==0, lst) Filter based on condition

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 Concept Example Purpose

reduce() reduce(lambda x,y: x+y, lst) Cumulative result (import from functools)

🔚 Final Tip: What to Practice Before Interview

OOP principles (class, inheritance, abstraction, polymorphism)
Functional tools (lambda, map, filter)

Data structures (list, set, dict)

Error handling (try-except)

Built-ins (zip, enumerate, sorted, any, all)

Would you like:

A 1-page revision sheet for these concepts?

Practice Python MCQs or coding questions tailored for AI/ML interviews?

Let me know and I’ll prepare those right away!

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