Course Content
Why Should You enroll?
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What is Python ka Chilla 2024?
10:16
Python seekh len Python ka Chilla is back #pythonkachilla
06:24
Enroll in LMS of Python ka Chilla 2024-25
05:52
Resources for the Course
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Books for the course
00:00
GitHub Repository for the course
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Day-1
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Course content in this course
05:50
Registration is still open
00:20
Know your instructor
04:52
Class Timings
00:00
Important Rules for the course
06:05
Turn on Video during meetings
01:36
What is Python?
03:23
LMS (Learning Management System) for the course
09:26
Zoom Meeting Questions and Answers
27:26
Day-2
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Installing different software to use Python for Data Science
12:43
VScode profile, extensions and themes for Python
06:31
Files and Folders management in VScode
09:36
GitBash and miniconda | Integration | Solutions
07:35
miniConda | Environments and Installations | A complete Guide
22:06
Day-3
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What we have learned so far and updates about the course
06:34
Python for Data Science and popularity of python
14:22
Important Logins to make for Python for Data Science
13:09
Access GitHub student or teacher developer pack
00:55
Naming conventions and rules for Python for Data Scientists
16:20
Ads in videos and Website
00:59
Jupyter notebooks | .ipynb vs. .py files
13:27
Jupyter Notebooks or .ipynb files for Data Scientists
07:31
MarkDown Language in 72 Minutes
01:11:23
Day-4
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Variables in Python
19:38
Types of Variables in Python
23:47
Type casting in Python (1/2)
23:41
Type Casting in Python (2/2)
09:03
Day-5
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Commenting in Python is important
04:58
git bash in vscode and conda in gitbash
03:44
Screen-casting mode in VSCode to show keyboard
02:29
Debugging is an art you need to learn
06:45
Day-6
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Google Your Errors and Answers to most common errors
10:00
Alternatives to GitHub copilots for VScode | Blackbox and codeium
03:05
Activating GitHub copilot while in the university
01:17
Do not share any link to anyone Plzzzzzzz
00:58
Zoom Meeting (Recording) for Python ka chilla 2024 on 06 10 2024
01:11:42
Day-7
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Windows screenshots and Screen Recording tool free
03:49
When to use which variable or data structure in python
10:49
Data Structures in Python | Complete Guide
35:50
Day-8
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AI Pioneers Win Nobel Prize in Physics 2024: Revolutionizing Machine Learning and Neural Network
13:08
Sequences in python
21:57
Indexing in Python
25:10
Slicing in Python
06:02
Mutable elements in Python
06:04
Day-9
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Range Function in python
02:25
TWO Nobel Prizes to AI
19:06
gitbash as default terminal in vscode
01:44
Day-10
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Maslay hal kar deay gaye hyn
03:54
Control Flow Statements in Python
20:06
Audience desi examples of Control Flow Statements
03:08
If else and elif control flow statements in python
17:35
Conditional and relational operators | nested if statements
21:56
Assignment alert To be submitted before next class
01:03
Questions and answer session | Zoom Meeting
05:07
Day-11
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for and while loops
15:15
break, continue and pass statements
08:45
Infinite loop in python | Beware of infinite loops
10:00
Try, Except and finally statements
05:16
Summary of control flow statements and assignment
02:34
Day-12
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Convert notebook to pdf | step by step guide with error handling (uncut version)
23:29
Functions in Python
26:22
Day-13
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Lambda Functions in python
22:32
user input in python
24:31
gitbash and conda in vscode
01:43
Day-14
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Future of Data Science | A podcast with Dr. Zeeshan ul Hassan Usmani
57:50
Day-15
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Top nine methods of using print( ) functions in python
41:55
Tip to improve communication
01:24
Generators in Python
13:34
Introductions to libraries in python
22:11
Social media presence is important for data Scientist
06:12
Day-16 (No lectures- Take Rest/Revise and enjoy the day)
Day-17
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PyPI , pip and famous libraries in python
04:25
Step-by-Step Installation of libraries in conda environments
16:33
Day-18
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Top five libraries of Python for Data Analysts
08:17
Seventeen important python libraries, I bet you did not know before
11:12
Day-19
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AI News of October 2024
07:25
Day-20
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Newsletters to access latest AI News
05:26
Using python libraries as professionals
16:34
Importing, finding, writing and Saving Data in python
19:55
Nine-step guide for Data Analysts and Data Scientists
20:17
Four dimensions of Data Handling and EDA for Data Scientists and Analysts
15:43
Day-21 (Automatics EDA-1)
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pandas profiling or y-dataprofiling for automatic EDA in python
20:50
python library for automatic exploratory data analysis for any data
14:03
Make stunning plots for EDA | like tableau for free in python
11:29
Day-22 (Automatic EDA-2)
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Sweetviz library for EDA with complete debugging uncut
36:01
Skimpy library, dealing with errors and installations (Practice)
22:43
Skimpy error resolved and working fine
04:29
lida for automatic EDA using LLM
09:54
Day-23 (Automatic EDA-3)
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Low code EDA library for python | dataprep | A-Z master guide
09:42
Day-24 (AI-News and Automatic EDA)
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Claude anthropic new model can use your computer
03:53
Google news Categories to search for latest AI and tech news
03:22
Lida python library for Automatic EDA using LLMs
20:23
Free AI Models available for LLM tasks, we will see in future
04:27
Account to make which are important for all
01:22
Day-25 (Pandas-1)
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Pandas Dataframes vs. datasets
14:16
Reading Data for python using pandas library from clipboard
06:24
Reading data from Excel using pandas library in python
06:41
Reading csv and tsv files in python using pandas library
12:08
Making dummy datasets in python using pandas library from dictionary
03:44
Writing data into files using pandas library in python
05:16
Assignment alert
02:05
Advance Data Import in Pandas using Excel workbook with specific paths
06:24
Excel vs. csv files in pandas data frame for python
04:59
Using absolute paths in windows for data import in pandas or python
03:19
Do not use spaces in reading files in pandas but what if?
02:06
Web scrapping using pandas library in python for table import
06:39
Read online .csv files using pandas python library
05:38
Day-26 (Pandas-2)
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Read tabular data in pandas
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Read data from json file in pandas python
01:17
Elements of Pandas dataframes in python | series | df
04:19
Subset or filter the data based on rows or columns
15:38
Important methods for selecting subset of the dataframes in pandas python
03:29
Selecting known data or unknown data from dataframes in pandas python
02:24
Day-27 (Pandas-3)
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Most important tasks
01:53
Absolute vs. relative paths to files and directories in data analysis
05:30
Day-28
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loc and iloc functions for data selection in pandas pyhton library
15:48
Day-29
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Fifty plus advance pandas commands for data analysis in python
01:17:07
Next Tasks for all to practice Pandas
03:00
Day-30 (Take a break and revise your previous lessons)
Day-31: Statistics for Data Science-(Part-1)
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Statistics for Data Science Crash Course
01:05:18
More lectures on statistics will be coming soon
00:46
LMS will be available for you for longer time
02:12
Data Visualization is coming soon In Sha Allah
02:16
Reources to read and learn Statistics
00:00
Day-32: Statistics for Data Science-(Part-2)
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Statistics for Data Science Complete Crash course
14:15:35
Day-33: Statistics for Data Science-(Part-3)
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Practice the previous Crash Course on Statistics
00:00
Day-34: Statistics for Data Science- (Part-4)
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Practice Crash Course on Statistics for Data Science
00:00
Zoom Meeting Recording (Day34 Python ka chilla 2024)
12:15
Day-35: Statistics for Data Science- (Part-5)
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Practice Crash course on Statistics
00:00
Codes are available here for statistics course
01:51
GitHub will be coming next
00:31
Day-36: Mathematics for Data Science and Machine Learning
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Mathematics for Data Science and Machine Learning
15:15:51
Day-37, 38, 39, 40: NumPy for Data Science
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Meditate and relax yourself before moving forward
03:41
Numpy for Data Science
34:58
50 plus commands for NumPy for Data Science and Machine Learning
57:35
Assignment and Next Tasks
01:47
Mathematics for Data Science and Machine Learning
00:00
Quizes
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Python for Data Science Quiz
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Mathematics for Data Science Quiz
00:00
Day-41: Data Visualization (Part-1)
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Attempt these Quizzes
02:28
Introduction to Data Visualization
10:41
Data Visualization in python | Important Terminologies
01:04:41
Stacking vs. Concatenation in NumPy
04:27
Tasks before next lecture
00:56
Day-42: Data Visualization (Part-2)
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Are you completing your assignments?
00:46
Data Visualization Theory and parts of plots
01:00:21
Day-43: Data Visualization (Part-3)
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Data Visualization using matplotlib and seaborn in python
01:07:33
Next Task
00:33
Day-44: Data Visualization (Part-4)
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Customization for plot parts in Data Visualization
19:28
Day-45: Data Visualization (Part-5)- Plotly Master Class-I
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Plotly Master Class for Data Visualziation in Python
52:40
3D Interactive plots in Plotly
08:34
Amazing Plots I bet you have never seen | Plotly Python
29:47
Day-46: Data Visualization (Part-5)- Plotly Master Class-II
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Marginal Plots in plotly
07:15
Animated Plots and saving as video files using plotly
17:10
Multiple advance plots using plotly
11:02
Day-47: Data Analysis and EDA complete project on real world data
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Data Analysis Complete Project using World Bank Datasets
02:01:21
Kaggle Guide to become a Grand Master
38:44
Day-48: Data Visualization (Part-6)- GeoMaps using Plolty
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Draw Geospatial Data Using Plotly
34:27
Task for Students to master Bokeh and Altair
04:40
How to upload the task on kaggle
00:34
Day-49: Data Visualization (Part-7)- Bokeh Library
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Basics of Bokeh Library for Data Visualization
24:44
Day-50: Magic Commands Used in Python
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Magic Commands %% used in Python
26:52
Day-51: Data Gathering and Data Scrapping
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Wordbank Data Gathering Library
20:16
FAOSTAT data download using python library
17:48
EUROSTAT data download using python
19:13
Assignment Alert
00:59
Yahoo Finance Stock Market Data with Python | yfinance Library Tutorial
12:18
Day-52: Machine Learning is Next
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Machine Learning is Next
00:41
Day-53: Machine Learning (Part-1)
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Machine Learning vs. AI vs. Deep Learning vs. Data Science
19:39
Types of Machine Learning
10:01
Day-54: Machine Learning (Part-2)
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Introduction to Machine Learning with detailed examples
11:46
Supervised Machine Learning
18:20
Unsupervised Machine Learning
08:15
Semi-supervised Machine Learning
00:59
Reinforcement Learning
07:50
Day-55: Machine Learning (Part-3)
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Applications of Machine Learning in real world
06:51
Data is important in machine learning
05:07
Scikit learn working and machine learning
05:56
Python for Machine Learning using Scikit learn
26:03
Day-56: Machine Learning (Part-4)
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Intermediate Machine Learning using Scikit-learn
36:31
Regression and Classification Metrics in Machine Learning
19:54
ML metrics for Classification and Regression Scikit-learn in Python
10:02
Day-57: Machine Learning (Part-5)
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Machine Learning Model Building to Deployment | Steps A-Z
34:28
What is an algorithm?
08:25
Training and Testing Data, Features, Labels and Model
04:19
Overfitting vs. Underfitting of a model
10:14
Day-58: Machine Learning (Part-6)
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Data Pre-processing for Machine Learning
30:28
Steps in Data Pre-processing
07:23
Data Scaling and Normalization
21:44
Feature Scaling vs Normalization in details
09:03
Tips about Scaling and normalization
02:51
Day-59: Machine Learning (Part-7)
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Please complete the following lectures before going further
01:52
Imputing Missing values using basic and advanced methods in python
55:01
Day-60: Machine Learning (Part-8)
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Regression | Simple Linear Regression | Metrics of Regression
41:29
Logistic Regression does classification
08:08
Regression vs. Classification metrics
08:13
Testing Data Matters do not ignore this
20:32
Support Vector Machines (SVM) | Machine Learning Algorithm
23:49
Day-61: Machine Learning (Part-9)
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K-Nearest Neighbours (KNN) | A machine Learning Algorithm
21:48
Euclidean Distance in KNN machine learning
19:00
Manhattan Distance in Machine Learning
09:10
Minkowski Distance in Machine Learning
08:56
Why Minkowski Distance is Important in Machine Learning?
03:44
Hamming Distance in Machine Learning
04:55
Algorithms you have learned so far
07:46
Decision tree Algorithm in Machine Learning
09:33
Elements of Decision Tree
12:52
Entropy, gini impurity and information gain theory
22:09
Day-62: Machine Learning (Part-10)
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Ensemble Algorithms in machine learning
21:22
Ensemble Algorithms Family
08:00
Random Forest Algorithm
18:55
Boosting Technique in Ensemble Methods
21:29
Boosting Algorithms in Machine Learning
11:48
Can boosting algorithms be better than Neural Networks
13:30
Evaluation Metrics in Machine Learning
04:31
Metrics to be used for Regression Tasks
19:23
Evaluation Metrics for Classification Algorithms
31:00
Day-63: Machine Learning (Part-11)
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Complete Previous Tasks as soon as possible
00:47
Removing Outliers in Dataset
16:23
Data Scaling and Preprocessing
18:40
Data Transformation
04:56
Data Normalization
11:07
Pipelines in Machine Learning Concept is important
07:18
Pipelines in Python with coding
10:56
Day-64: Machine Learning (Part-12)
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What is feature encoding?
13:06
Why do we need feature encoding?
12:44
Feature Encoding in Python | Learn One-Hot, Label Encoding & More!
12:20
Day-65: Machine Learning (Part-13)-Data Preprocessing Crash Course
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Master Data Preprocessing, Wrangling, and Cleaning for Machine Learning Projects!
04:54:16
Day-66: Machine Learning (Part-14)
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Intermediate Use of sk-learn to improve the ML skills
25:14
Day-67: Machine Learning (Part-15)
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How to improve the ML model performance?
14:28
Day-68: Machine Learning (Part-16)
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Polynomial Regression
08:25
Day-69: Machine Learning (Part-17)
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Kaggle.com is an important website follow these trends now
09:10
Ridge Regression | L2 Regularization
25:34
Lasso Regression | GridSearch CV and RandomizedSearch CV
29:38
Day-70: Machine Learning (Part-18)
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Logistic Regression and classification metrics
19:14
KNN (K Nearest Neighbour) Regressor and Classifier
12:51
Day-71: Machine Learning (Part-19)
0/1
Support Vector Machines in Python for Regression and Classification
10:10
Day-72: Machine Learning (Part-20)
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Decision Trees in Python
15:28
Random Forest Algorithms in Python for Machine Learning
04:20
Bagging and Boosting Algorithms in Python for Machine Learning
07:21
Day 73: Machine Learning (Part-21)
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CatBoost Algorithm in Python for Classification in Machine Learning
12:44
Naive Bayes Algorithm (Part-1)
16:17
Types of Naive Bayes Algorithm | Naive Bayes (Part-2)
06:18
Naive Bayes Algorithm | Python for Machine Learning
03:47
Day 74: Machine Learning (Part-22)
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Cross Validation Techniques in Python | Evaluate Machine Learning Models with Sklearn
12:34
Hyperparameter Tuning for Machine Learning Models in Python | Sklearn
16:14
Best Model Selection for Regression & Classification in Python | Machine Learning with Sklearn
18:51
Important Machine Learning models to Practice using Python
Day 75: Auto Machine Learning for Best Models (Part-23)
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PyCaret Tutorial for Automated Machine Learning | Simplify ML Tasks with Python
39:44
PyCaret for Automated Regression Tasks | Complete Guide to Machine Learning with Python
16:48
Day 76: Deep Learning Complete Courses
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Deep Learning Crash Course Part-1 | Master Neural Networks & AI Fundamentals
10:36:35
Deep Learning Crash Course Part-2 | Master Neural Networks & AI Fundamentals
11:33:17
Day 77: Git & GitHub Tools
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git & GitHub for Data Scientists and AI experts
47:18
GPT4o-copilot | Vibe coding in VScode (Visual Studio Code)
09:22
Day 78: Time Series Data and Data Analysis + Web scraping
0/1
Time Series Data Analysis & Forecasting using ML/DL + Web Scraping | Crash Course | Urdu/Hindi | Under 11 Hours
10:44:14
GenAI and Large Language Models and RAG based apps
0/13
Generative AI and its applications
30:36
Online Earning using GenAI tools and applications
26:20
APIs to make tools and Applications | Application programing interface and GenAI
18:13
GenAI vs. Agentic AI in a nutshell | Complete guide
13:58
100+ GenAI Application Ideas to Level Up Your Portfolio
06:54
What Are Large Language Models (LLMs) & How They Work – Explained
16:32
LLM vs. Fine-Tuned LLM vs. RAG-based LLM – Complete Guide
18:38
RAG (Retrieval-Augmented Generation) Explained for LLMs
12:12
Augmented Generation for Better LLMs – RAG vs. CAG Explained
25:26
Project Ideas to earn online using RAG and CAG bases applications and tools
23:36
Ten Lessons for Enterprise level RAG (Retrieval Augmented Generation) based apps
19:14
What’s MCP All About? | MCP server is new buzz word in AI agents
25:02
GPT-o3 advance reasoning model to locate someone
13:32
VScode and Agentic AI
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VScode as an IDE | Handon functions
43:10
Why VScode is my favorite IDE for Data Science and AI workflow?
36:08
Day 80: Summary
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More lectures coming soon
10:43
Next 10 days you can plan and revise
02:17
Advance Course on Data Science will be coming soon
10:58
What did you learn in this course? Zoom Meeting with Students
55:18
Feedback from students
14:37
Complete Projects A-Z
0/4
Basic EDA on Titanic Dataset complete project
01:07:31
Coding Night for Data Science and Data Analytics Project
03:21:15
Save plotly animation as video for reels and YouTube videos
18:20
Coding Night: Live Machine Learning Masterclass in Python | Learn & Code Together!
01:54:03
Portfolio building guide
0/1
Build Your AI & Data Science Portfolio
02:37:54
Python Ka Chilla 2024-2025
    Join the conversation
    Amir Altaf 2 months ago
    To compete the World in Technology we realy needs to learn advance things and upgrade with the time to time. In this regard I want to learn the complete course to serve the nation/country and obviously for my career aswell.
    Reply
    Safi Ullah 11 months ago
    I want to learn AI,, becasue I have felt myself too far in competition.. each day, when i have some repititive task, people do it using AI, or using python, they create custom script for themselves, and they increase their productivity...I am learning AI to embrace ai, to increase my productivity, workflow...
    Reply
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