LIST | List in Python | Hinglish #listinpython #python #datascience

The Data Science course equips learners with skills to extract insights, make data-driven
decisions, and solve real-world problems. Starting with Python fundamentals and statistics, it
covers data manipulation (NumPy, Pandas), visualization (Matplotlib, Seaborn), and database
management (SQL, NoSQL).
Key topics include machine learning (regression, decision trees, SVMs, Random Forests), deep
learning (neural networks, CNNs, RNNs, GANs), and NLP (BERT, GPT). Learners gain
hands-on experience with tools like TensorFlow, PyTorch, Hugging Face, and LangChain,
tackling projects such as customer segmentation, image classification, and text summarization.
The course also explores cutting-edge AI topics like LLMs, prompt engineering, and RAG,
ensuring mastery of the end-to-end data science workflow for a successful career in AI.

Tools & Technologies
● Python: Core programming language for development and automation.
● Python IDEs: PyCharm, VS Code, Jupyter Notebook, Google Colab, Deepnote
● Data Analysis & Visualization: NumPy, Pandas, Matplotlib, Seaborn, Plotly, Bokeh.
● Databases: MySQL, MongoDB
● API Development & Testing: Flask, Postman
● Machine Learning & Deep Learning:
○ Frameworks: Scikit-learn, TensorFlow, Keras, PyTorch.
○ Libraries: XGBoost, CatBoost, Hugging Face, OpenCV.
● Computer Vision: YOLOv9, Mask R-CNN, Detectron2, OpenCV, Roboflow.
● Natural Language Processing (NLP):
○ Libraries: NLTK, spaCy, TextBlob.
○ Tools: LangChain, LlamaIndex, Hugging Face Hub.
● Generative AI:
○ Frameworks: GPTs, Autoencoders, LangChain, ChromaDB.
○ Deployment Tools: Streamlit

● Data Types & Structures: Explores strings, lists, tuples, dictionaries, and sets, focusing
on their properties, manipulation techniques, and practical applications.

Expected Learning Outcomes
● Gain proficiency in Python programming fundamentals.
● Understand data structures and their applications.
● Develop modular and reusable code using functions.
● Implement object-oriented principles for efficient coding.
● Handle files, exceptions, and logs proficiently.
● Use NumPy and Pandas for data analysis.
● Build and deploy RESTful APIs using Flask.
● Work with SQL and NoSQL databases, including MongoDB.

list
list in python
what is list
what is list in python
use of list in python
use of list
list kya hain
list kya hota hain
list kya karta hain
what is work of list in python
data types in python
data type
data type and structure
tuple
string
set
dictionary

data science
python
oops
java
c++
coding
sql
vs studio
collab

your fault
you fault
you flt
u flt
you flat
your flat
you falat
your falat
u flat
u falat