Course Content
Why Should You enroll?
0/3
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
00:00
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
0/5
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
0/9
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
0/4
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
0/4
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
0/5
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
0/5
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)
0/1
Low code EDA library for python | dataprep | A-Z master guide
09:42
Day-24 (AI-News and Automatic EDA)
0/5
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
03:41
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)
0/5
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)
0/1
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
00:00
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)
0/2
Are you completing your assignments?
00:46
Data Visualization Theory and parts of plots
01:00:21
Day-43: Data Visualization (Part-3)
0/2
Data Visualization using matplotlib and seaborn in python
01:07:33
Next Task
00:33
Day-44: Data Visualization (Part-4)
0/1
Customization for plot parts in Data Visualization
19:28
Day-45: Data Visualization (Part-5)- Plotly Master Class-I
0/3
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
0/3
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
0/2
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
0/3
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
0/1
Basics of Bokeh Library for Data Visualization
24:44
Day-50: Magic Commands Used in Python
0/1
Magic Commands %% used in Python
26:52
Day-51: Data Gathering and Data Scrapping
0/5
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
0/1
Machine Learning is Next
00:41
Day-53: Machine Learning (Part-1)
0/2
Machine Learning vs. AI vs. Deep Learning vs. Data Science
19:39
Types of Machine Learning
10:01
Day-54: Machine Learning (Part-2)
0/5
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)
0/4
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)
0/3
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)
0/4
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)
0/2
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)
0/5
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)
0/10
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)
0/9
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)
0/7
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)
0/3
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
0/1
Master Data Preprocessing, Wrangling, and Cleaning for Machine Learning Projects!
04:54:16
Day-66: Machine Learning (Part-14)
0/1
Intermediate Use of sk-learn to improve the ML skills
25:14
Day-67: Machine Learning (Part-15)
0/1
How to improve the ML model performance?
14:28
Day-68: Machine Learning (Part-16)
0/1
Polynomial Regression
08:25
Day-69: Machine Learning (Part-17)
0/3
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)
0/2
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)
0/3
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)
0/4
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)
0/4
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)
0/2
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
0/2
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
0/2
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
0/2
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
0/5
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
About Lesson

Link to miniconda command cheatsheet: https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf

Join the conversation
Syed Fakhr E Ali 9 months ago
Baba g tussi great o!
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Syed Fakhr E Ali 9 months ago
0:04:58 Yes.
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Syed Fakhr E Ali 9 months ago
0:01:08 Kitchen
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Syed Fakhr E Ali 9 months ago
0:01:08 Bedroom
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Syed Fakhr E Ali 9 months ago
0:01:02 Drawing Room
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Maria Anwar 10 months ago
in Git bash using remove command ....it is not removing the environment we created in miniconda... ???
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Muzammil Abid 10 months ago
If Still Git bash is not your default server Then little "down arrow" sign near + sign and click "select default profile"
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Shoaib Akbar 10 months ago
In VS Code, there was only one environment available and that was of miniconda and I have to select that eventually, once done now the results of two lines you asked us to write is appearing in the Python REPL.In Bash: there is no Base written before the command prompt and whatever I am writing it is showing conda command not found.
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