What you'll learn

Basic Introduction of the Data Science in HINDI/URDU language
Basic Introduction of the Machine Learning
Learn about the tools needed for the AI projects
AI project Life Cycle

Requirements

A Smile..:)
HINDI/URDU language

Description

Are you Interested in the field of Machine Learning or Data Science, Then this course is for you!

This Course is designed to give you a good understanding about the Data Science Field , In this Course you will learn about the Basics of Data Science Machine Learning and Artificial Intelligence.

YOu will also learn about the complete life cycle of the Machine Learning Projects.

Course content

–Data Science and Machine Learning

What is Machine Learning.

Data Science Play Ground

First Image Classifier.

Recommended System using K nearst Means

Data Science vs Machine Learning vs Artificial Intelligence

Summarizing it all

–AI Project Life Cycle

AI Project Framework

Step-1 Problem Definition

Step-2 Data

Step-3 Evaluation.

Step-4 Features

Step-5 Modelling.

Step-5 Data Validation

Step-6 Course Correction

Tools needed for AI Project

This course is still in a draft mode. I am still adding more and more content, quiz, projects related to data processing with different functionalities of Pandas, Sklearn and Many More Projects. So stay tuned and enroll now.

Here is what other students Like you say about this course.

Durga Nand Choudhary

¨best course¨ 5 stars

Mohd Naved

¨very nice course thankyou for creatig this course¨ 5 stars

Abhishek Tiwari

¨sir really good course when i start this course i am very inspired of this course thanks u very much sir¨ 5 stars

AI Khan

¨easy to comprehend¨ 5 stars

Birader Omkar

¨This is the masterpiece course. I recommend everyone to join this journey. I am eagerly waiting for next chapters to release. Thank you sir¨ 5 stars

I have 100% response rate , so I will always be there to respond to your questions. And of course after Completing this course not only you will built your portfolio but also get Certificate of completion which you can post on your linked Profile and Attract potential Employers.

Who this course is for:

Python Developers, Looking insight into Data Science
Anyone interested in Machine Learning.
Any students in college who want to start a career in Data Science

12 sections • 226 lectures • 27h 37m total length

Preview06:15
Preview07:20
First Image CLassifier.
06:55
Preview00:00

What is Programming Language
06:09
Python Interpreter and First Code
07:45
Python 3 vs Python 2
04:36
Formula to Learn Coding
04:12
Data Types and Basic Arithmatic
10:25
Basic Arithmetic Part-2
05:59
Rule of Programming
04:50
Mathematical Operators and Order of Precedence
05:05
Variables and their BIG No No
10:00
Statement vs Expression
03:18
Augmented Assignment Operator
03:14
String Data Type
06:01
String Concatenation
09:08
type Conversion
04:53
String Formatting
07:15
Indexing
09:55
Immutability
03:39
Built in Function and Methods
06:31
Boolean Data Type
02:54
Exercise
03:52
Data Structor and Lists
08:24
Lists continued
10:04
Matrix from Lists
03:40
List Methods
08:19
Lists Methods 2
09:01
creating Lists Programatically
05:03
Dictionary
08:44
Dic key is Un Changeable
05:11
Most Used Methods on Dictionaries
07:40
Tuple Data Types
09:11
Sets data Types
08:19
Intro to Process of Coding-Conditionals
01:56
if else Statement
11:06
AND OR keywords
06:11
Boolean result of Different values
04:54
Logical Operators
07:53
Identity Operator
05:01
for loop and Iterables
11:20
Nested For loop
05:50
Exercise for loop
03:45
Range Function
07:54
While Loop
07:15
Continue Break Pass Keywords
06:25
Exercise Draw a Shape
03:34

Functions
05:49
Why of Functions
04:51
Parameter vs Argument
07:01
Default Parameters
07:09
Return Keyword
05:56
Doc String
07:28
Good Programming Practices
06:22
args and kwargs
06:49
Exercise
10:58
Scope of a Function
05:41
Scope Rules-1
08:25
Scope Rules-2
03:18
GLobal vs nonlocal Keywords
05:43
Programming Best Practices-2
07:07
Special Functions map
06:56
Special Functions Filter.
06:58
Special Functions Zip
04:13
Special Functions reduce
08:01
List Comprehension Case-1,2 and 3
10:38
Sets and Dictionary Comprehension
03:31
Python Modules
09:41
Python packages
08:04

matplotlib Into
05:05
First Plot with matplotlib
07:12
Methods to Plot
08:14
settingup Features
03:57
One Figure Many Plots
09:50
Most Used Plots Bar plot
08:55
Histogram
07:46
Four plot one figure
03:34
Pandas Data Frame
08:32
Plotting from Pandas Data Frame
11:21
Bar plot from Pandas Data Frame
09:17
pyplot vs OO methods
10:22
Life Cycle of OO method
10:20
Life Cycle of OO method Advanced
11:59
Customization Part-2
02:30
Customization Part-3
03:57
Figure Styling
04:20
Naming Entire Figure
06:17

What Actually ML Model is
06:53
Intro to Sklearn
06:39
Step-1 Getting Data Ready Split Data
08:08
Step-2 Choosing ML model
05:04
Step-3 Fit Model
03:37
Step-4 Evaluate Model
05:38
Step-5 Improve Model
05:06
Step-6 Save Model.
06:37
What we are going to Do
08:25
Step-1 Getting Data Split Data
07:18
Step-1 Getting Data Ready Converting Part-1
06:48
Getting Data Ready Converting Part-2
08:49
Getting Data Anatomy of Conversion
05:12
Getting Data Second Method of Conversion
04:27
Getting Data Missing Values.
08:01
Getting Data Missing Values method 2
14:45
Choosing Machine Learning Model
07:07
Using map to choose model
14:06
Step-2 How to Choose Better model
06:22
Choosing Model for Classification problem
14:45
Fit the Model
04:48
Running Prediction
14:01
Step-3 predict_proba method
05:40
Step-3 Running Prediction on Regression Problem
09:14
Step-4 Evaluating Machine Learning Model Default Scoring
12:43
Step-4 WHat is Cross Validation
15:22
Step-4 Accuracy (Classification Model)
04:18
Step-4 Area Under the Curve Part-1
08:49
Step-4 Area Under the Curve Part-2.
08:23
Step-4 Area Under the Curve Part-3 Plotting
08:55
Confusion Matrix Calculate
12:14
Step-4 Confusion Matrix Plot
09:05
Step-4 Classification Report Important concepts
07:55
Step-4 Classification Report Fully Explained
07:45
Step-4 R2 for Regression Problems
06:57
Step-4 Mean Absolute Error for Regression Problems
06:37
Step-4 Mean Square Error for Regression Problems
06:34
Step-4 Scoring parameters for Classification
06:31
Step-4 Scoring parameters for Regression
04:37
Step-4 Evaluation using Functions Classification
10:46
Step-4 Evaluation using Functions Regression
10:55
Step-5 Improving Model by Hyper parameters
06:34
Step-5 Improving Model by Hyperparameters manually
08:34
Step-5 Hyperparameters Task-1
09:20
Step-5 Evaluation Metrics in One Function
08:24
Step-5 Hyperparameters Comparison
04:43
Tunning Hyperparameters using RSCV
14:34
Tunning Hyperparameters using RSCV Part-2
11:44
Tunning Hyperparameters using GSCV
10:37
Results Comparison
07:57
Save Load Model with Pickle Method-1
06:52
Save Load Model with joblib Method-2
04:28

Dr. Ahmad Mohsin PhD

Engineer ,Programmer and Tutor

4.4 Instructor Rating
1,900 Reviews
52,784 Students
10 Courses

Founder of datascience.university. Mohsin Khalil Ahmad Served as Lecturer in Well reputed University and is linked to Academia for last 6 years. He is PhD in aerospace engineering. He has been using Python for scientific computing, Data Analysis and Machine Learning for last 10 years.

Mr. Mohsin Khalil have worked as Research Engineer in AeroTraNet2 Project which Included top European Aerospace Industries Like Airbus France, Von Karman Institute for Fluid Dynamics(VKI) Brussels and Universities like Uni Leicester London,UK

Mr. Mohsin have won numerous awards, including ,Erasmas Mundas Scholar, Marie Curie Fellow, Italian National Research Award and counting and have authored numerous research articles.

You are being taught by the best of the best.

Easy Course in Machine Learning and Artificial Intelligence

What you'll learn

Requirements

Description

Who this course is for:

Course content

Introduction4 lectures • 21min

Data Science and Machine Learning3 lectures • 18min

AI Project Life Cycle9 lectures • 1hr 7min

Python the Most Powerful Language44 lectures • 4hr 46min

Python Part-222 lectures • 2hr 31min

Environment Setup for Machine Learning Projects12 lectures • 1hr 8min

Pandas for Data Analysis10 lectures • 1hr 40min

NumPy17 lectures • 2hr 34min

Matplotlib18 lectures • 2hr 13min

Scikit-Learn52 lectures • 7hr 10min

Instructor