Course Content
Day-1: Welcome and Introduction
This topic will cover the basic introduction of this course and software installation.
0/4
Who is a Data Scientist?
03:20
Introduction to python ka chilla for Data Science
01:00:13
Installation of Software needed for the course
05:37
Write your first line of code
06:42
Day-2: How to use VScode (an IDE) for Python?
0/1
Learn VScode for python
01:01:41
Day-3: Basics of Python Programming
This section will train you for Python programming language
0/4
Python-101
01:03:45
Python-101 Extended
01:55:14
Python-101 Extended (Again)
01:55:14
Python-101-Beyond functions
01:04:46
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
0/1
Data Visualization and Jupyter Notebooks
01:16:56
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
0/1
Markdown Language Crash Course
01:11:23
Day-6 to Day-9: Pandas Library
You will learn basics to advanced use of Pandas library in this section.
0/4
Pandas-Import Dataset and basics function
01:15:43
Pandas-More Pandas Functions
11:31
Pandas-Tips and Tricks (Part-1)
02:05:25
Pandas-Tips and Tricks (Part-2)
01:19:44
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
0/1
Data Wrangling and Data Visualization
01:25:46
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
0/2
Data Visualization (Part-1)
16:06
Data Visualization (Part-2)
27:55
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
0/2
EDA (Part-1)
01:17:20
EDA (Part-2)
01:51:15
Day-14: ABC of Statistics for Data Science
In this section you will learn ABC of Statistics (From a beginner's perspective) with Desi Tarkay wali examples.
0/9
ABC of Statistics (Part-1)
34:23
ABC of Statistics (Part-2)
01:01:44
ABC of Statistics (Part-3)
59:08
ABC of Statistics (Part-4)
58:53
ABC of Statistics (Part-5)
01:04:17
ABC of Statistics (Part-6)
01:00:39
ABC of Statistics (Part-7)
58:21
ABC of Statistics (Part-8)
00:00
ABC of Statistics (Part-9)
01:13:54
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
0/1
Python Data Wrangling Techniques: From Beginner to Pro
01:59:35
Day-16 to Day-25: Machine Learning
In This Section for next 10 Days we will Learn Machine Learning.
0/26
What is Machine Learning?
01:29:04
Basics of Machine Learning
01:39:00
Regression
01:06:06
Classification
01:40:26
Machine Learning with Scikit-learn in Python
23:33
Hyperparameter Tuning with GridSearchCV
01:09:37
Machine Learning Terminologies
50:34
Machine Learning in another perspective
10:56
Cross Validation
11:18
Confusion Matrix
09:21
Sensitivity vs. Specificity
06:55
Bias Variance
08:04
Entropy
19:13
Linear Regression
13:55
Multiple Linear Regression
07:17
Logistic Regression
09:25
ROC and AUC
19:11
Maximum Likelihood
15:52
R-square for Logistic Regression
07:11
Ridge Regression
12:58
Lasso Regression
04:41
Elastic-Net Regression
04:56
Principal Component Analysis (PCA)
17:54
K-means Clustering
12:55
How to save and use a trained ML Model?
34:31
Practice on Machine Learning Real Projects
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
0/1
Conda Environments
54:41
Day-27 to Day 35: Deep Learning with Tensorflow
We will learn using tensorflow for Deep Learning
0/9
Intro to Tensorflow
01:23:36
Neural Network
20:34
Intro to Computer Vision
00:00
Image Classification with TensorFlow
40:36
Activation Functions (Part-1)
42:04
Activation Functions (Part-2)
01:15:30
Project on Tensoflow
33:30
How many Epochs should we use?
25:16
Types of Neural Network
17:36
Day-36: DashBoard and Web apps development for Data Science
In this Section we will learn how to make dashboard and webapps for Data Science in Python.
0/14
Introduction to Streamlit
42:29
My First Webapp
36:20
Use plotly in DashBoards
12:21
Animated Plots in Dashboard
02:18
EDA Webapp Development
23:39
Streamlit k Jugar
07:49
ML Web App with Streamlit
19:27
WebApp Deployment on Streamlit
08:47
Add Media files to webapps
11:04
Add Codes to your webapps
03:48
Make Interactive Explainer DashBoard
07:00
Embed code snippets into DashBoards
09:13
Streamlit Project for Data Science (Part-1)
01:11:49
Streamlit Project for Data Science (Part-2)
24:04
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
0/2
Time Series Analysis (Part-1)
01:05:14
Time Series Analysis (Part-2)
24:11
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
0/2
NLP and Text Classification project
34:14
Tensorboard
41:00
Day-39: git and github
We will learn about git and github
0/1
All about git and GitHub
47:41
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
0/1
ChatGPT for Twitter and other Social Media Platforms
40:55
Feed Back
0/1
FeedBack and Review on the course
00:00
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Key reasons why creating conda environments is necessary/useful:

  • Dependency and version management: Conda environments allow you to isolate different projects that may have conflicting dependencies or require different versions of packages. This avoids dependency clashes.

  • Reproducibility: Environments make it easy to exactly replicate the software environment used for a project/analysis. This ensures reproducible results.

  • Code sharing: Environments allow sharing code/notebooks with others while bundling all dependencies. Avoid issues caused by missing or wrong package versions.

  • Testing different stacks: You can test different package combinations and Python/IPython versions easily using separate environments without interfering with system setup.

  • Package conflicts: Some packages conflict in how they use or install dependencies. Environments prevent such issues from arising.

  • Clean project separation: Keeping each project activitiesisolated in its own environment prevents namespace pollution and clutter. Easy to switch between projects.

  • System stability: Environments avoid the risk of new package versions breaking existing projects or the system Python setup.

  • Temporary setups: Useful for experimenting with packages without permanently changing the system configuration. Easy clean up by deleting env.

So in summary, conda environments make dependency and version management clean and reproducible for both development and production environments.

Install miniconda from here

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komal Baloch 9 months ago
done
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Syed Abdul Qadir Gilani 10 months ago
MA SHA ALLAH, Sir
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