Python Fundamentals Course for Indian Beginners

Learn python from extreme basics also learn Data Science tools Numpy & Pandas
English [Auto]
Learn to use Python professionally in natural Hindi language
Learn how to work with data science tools like Numpy & Pandas
Get an understanding of how to use Google Colab
Build complete understanding about Python from extreme basics


  • Computer/Laptop with internet connection


Python course specially taught in natural Hindi language for a clear understanding of programming concepts through this beautiful programming language.

What you will learn

  1. Variables

  2. Strings

  3. Lists

  4. Tuples

  5. Sets

  6. Dictionaries

  7. Indentation Concepts

  8. Loops

  9. Functions

  10. Reading & Writing Notepad Text Files

  11. Numpy

  12. Pandas

This course is designed for extreme beginners. It is a straight forward course with minimized clutters to make learning fast and easy

Who this course is for:

  • Beginner python developer curious about data science
  • Python beginners want to explore
  • Programming learners

Course content

4 sections9 lectures1h 23m total length
  • Variables as storage boxes!
  • Play with strings!


Kushal Sharma
  • 4.2 Instructor Rating
  • 131 Reviews
  • 6,878 Students
  • 2 Courses

Kushal Sharma is a passive researcher in Machine Learning as well as a trainer. Kushal is the Founder and principal tutor at KAI Institute Of AI. Kushal had a lot of struggle in learning the things discussed below so he knows how to teach by matching the student level of knowledge.

A Brief Overview

Former Research Intern at Axis India Machine Learning Research Labs, Jaipur

Skills in applying intricate algorithms based on deep-dive statistical analysis and predictive data modeling that were used to deepen the relationships, strengthen longevity and personalize interactions with customers,

Proficiency in analyzing and processing complex data sets using advanced querying, visualization, and analytics tools.

Knowledgeable and detail-oriented in utilizing statistical models

Broad scientific and mathematical knowledge with the ability to apply learning to real-world situations

Research cum Professional Experience

Presented a research paper titled “Infant Weeping calls decoder using Statistical Feature Extraction and Gaussian Mixture Models” at “The Tenth International Conference on Computing, Communications, and Networking Technologies“ at IIT Kanpur.

In this research, he tried to decode the baby crying voices which can be so helpful for novice parents if they can get why their baby is crying which can save a lot of money and time that they spent at pediatricians.

Presented a research paper titled “Positive and Negative vibe classifier by converting two-dimensional image space into one-dimensional audio space using statistical techniques for feature extraction and deep learning for classifying” at the “Springer conference“ at NIT Kurukshetra, Haryana, India.

This paper describes how to classify negative and positive vibes in images which is based on converting images to audios. Also, this paper describes what features to extract in doing audio classification of these types of audio signals and classifying them using multilayer perceptron with special weight initialization and hyperparameters

Dean at Jaipur School Of AI

This is a non-profit agency run by Mr. Siraj Raval. Kushal Sharma manages the community of AI for the Jaipur region and has conducted many pro bono Classes for young Students

Remote Data Scientist, TVMucho, London, UK
Worked as a remote data scientist for TV Mucho for analyzing data on customer behavior.

Detailed Overview of the educator

Well versed with the ML Problems to the most appropriate ML Algorithms according to the task at hand: Prediction, Classification, and Clustering

Supervised Learning(Classification and Regression) Algorithms and Unsupervised Learning(Clustering and Dimensionality Reduction)

Deep Learning CNN, RNN, Feed Forward Neural Networks, Generative Adversarial Networks, Variational Autoencoders, good working knowledge of all the tools involved in making statistical inference

Different metrics involved in descriptive Univariate and Multivariate statistics

Frequentist Inference(Hypothesis Testing, ANNOVA)

Complete Knowledge of Python with Pandas, Numpy, Scikit Learn, Tensorflow, Keras, Scipy, PyTorch and can code all the ML algorithms without any pre-written python module

Data Pre-processing and Data Mining

Matrix Algebra(Singular and Non-Singular Matrices, Orthogonal Matrices, Inverse of a Matrix, Positive Definite Matrices and Negative Definite Matrices

Audio and Image Processing, Feature Extraction, Image/Signal Processing

Image Processing basic and advanced algorithms such as SIFT, SURF, Edge Detection, Hough Transformation(Line/Generalized), Dithering, Histogram Equalization