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30-Day Money-Back Guarantee
IT & Software IT Certification Data Science

Easy Course in Machine Learning and Artificial Intelligence

In HINDI/URDU LANGUAGE: Course in Machine Learning and Data Science. Enhance Your Understanding
Rating: 4.5 out of 54.5 (27 ratings)
551 students
Created by Dr. Ahmad Mohsin PhD
Last updated 11/2020
Hindi
30-Day Money-Back Guarantee

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

Course content

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

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

  • Recommender Systemt using K nearst Means
    04:17
  • Data Science vs Machine Learning vs Artificial Intelligence
    11:20
  • Sumarizing it all
    02:47

  • AI Project Framework
    11:41
  • STep-1 Problem Defination
    11:54
  • Step-2 Data
    05:35
  • Step-3 Evaluation.
    03:16
  • Step-4 Features
    05:52
  • Step-5 Modelling.
    10:05
  • Step-5 Data Validation
    08:39
  • Step-6 Course Correction
    04:27
  • Tools needed for AI Project
    05:39

  • 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

  • Tools for Data Science Environment
    06:53
  • Who is Mr. Conda
    03:03
  • Setting Up Machine Learning Project
    05:51
  • Blue Print of Machine Learning Project
    03:50
  • Installing conda
    05:51
  • Installing tools
    03:44
  • Starting Jupyter Notebook
    06:36
  • Installing for MacOS and Linux
    01:43
  • Walkthrough of Jupyter notebook 1
    08:10
  • Walkthrough of Jupyternotebook 2
    06:47
  • Loading and Visualizing Data
    11:15
  • Summing it Up
    04:06

  • Tools needed
    05:39
  • Pandas and What we Will cover
    03:08
  • Data Frames
    10:11
  • How to Import Data
    11:10
  • Describing Data
    07:07
  • Data Selection
    14:03
  • Data Selection 2
    14:15
  • Changing Data
    11:30
  • Add Remove Data
    12:33
  • Manipulating Data
    10:10

  • What and Why of Numpy
    07:06
  • Numpy Array
    14:25
  • Shape of Array
    08:29
  • Important Functions on Arrays
    08:51
  • Creating Numpy array
    13:00
  • random seed
    03:28
  • Accessing Elements
    12:29
  • Array Manipulation
    09:27
  • Aggregations
    08:44
  • mean variance and std
    06:35
  • Dot Product vs Matrix Manipulation
    04:32
  • Dot Product
    14:26
  • Reshape and Transpose
    04:52
  • Exercise
    14:46
  • Comparison Operators
    03:37
  • Sorting Arrays
    09:29
  • Reading Images
    09:26

  • 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

Instructor

Dr. Ahmad Mohsin PhD
Engineer ,Programmer and Tutor
Dr. Ahmad Mohsin PhD
  • 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.



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