Section outline

  • General

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    • forum icon
      Announcements Forum
      Welcome Message:

      In this Software Project II lab, we will introduce a new term, which is Artificial Intelligence(AI). Are you wondering about the future world? Yes, of course, Artificial Intelligence is going to rule in industries all over the world. Not only that, but it will play its role in household works also. This course aims to introduce yourself to the world of Artificial Intelligence. The focus of this lab course will include basics knowledge of python, ML algorithm, and neural network. These will be some of the essential fundamental knowledge for every computer scientist in the future.

      You are welcome to browse the page.

      Hope we will have a memorable journey together.

       Name Md. Zabirul Islam
       Lecturer, Dept of CSE
       Daffodil International University
       Email: zabirul.cse@diu.edu.bd
       Cell: 01521-307471 
       

       Course Objectives:

       After completing the course students will be able to:

      - Explain fundamentals of Python Language

      - Solve real-life problems using Python

      - Implement a few basic ML algorithms


      Assessment Policies:

      Class Attendance

                    10

      Quiz 

                  25

      Lab test

                     25

      Project + Assignment

                       40

       Total

      		                  100









      Grading Policy:

      Numerical Grade

      Letter Grade

      Grade Point

      80% and above

      A+

      (A Plus)

      4.0

      75% to less than 80%

      A

      (A regular)

      3.75

      70% to less than 75%

      A-

      (A minus)

      3.5

      65% to less than 70%

      B+

      (B Plus)

      3.25

      60% to less than 65%

      B

      (B regular)

      3.0

      55% to less than 60%

      B-

      (B minus)

      2.75

      50% to less than 55%

      C+

      (C Plus)

      2.5

      45% to less than 50%

      C

      C (regular)

      2.25

      40% to less than 45%

      D

       

      2.0

      Less than 40%

      F

       

      0.0
















      Reference Book: 

      1)Hands on Machine Learning with Scikit Learn and Tensorflow.pdf

      2) Python 3 Pdf - Tutorialspoint


    • Students must
      Mark as done

      Welcome Video:

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    • Profile Link

         

  • Week 1

    Introductory Class

    • url icon
      Lab class 1 (Introductory class)_L section (25-1-21) URL
    • url icon
      Lab class 1 (Introductory class) (26-1-21) URL

  • Week 2

    Session I: Introduction to Machine Learning

    Intended Learning Outcome:
    At the end of the session, the student should be able to:

    – know what is machine learning

    – know why we use it

    – know the real-life example


  • Week 3

    Session II: Basics of Python

    Intended Learning Outcome:
    At the end of the session, the student should be able to:

    • Discuss the three basics of Python

    – Syntax

    – Variables and data types

    - Conditions and Loops


  • Week 4

    Session II: Basics of Python

    Aim of this lecture:

      • The following topics will be discussed in the class

    – Condition and Loops

    - Function

    – List, tuples, dictionaries

    • url icon
      Class Video 4 (17-2-2021) URL
    • url icon
      Class Video 4 (18-2-2021) URL
    • forum icon
      Discussion on Python (Week 3) Forum
      Students must
      Mark as done
    • Students must
      Mark as done

      Additional Links

    • url icon
      Resource Link (Tuples) URL
      Students must
      Mark as done
    • url icon
      Resource Link (Set) URL
      Students must
      Mark as done
    • url icon
      Resource Link (List) URL
      Students must
      Mark as done
    • url icon
      Resource Link (Dictionaries) URL
      Students must
      Mark as done
    • Students must
      Mark as done
      Python Tutorial - Python for Beginners

  • Week 5

    • url icon
      Class Video on Sets and dictionary (24-2-2021) URL
    • url icon
      Class Video on Numpy (25-2-2021) URL
    • forum icon
      Discussion on Numpy(Week 4) Forum
      Students must
      Mark as done
    • Students must
      Mark as done

      Additional Links

    • url icon
      Resource Link (Numpy) URL
      Students must
      Mark as done
    • Students must
      Mark as done
      Python Machine Learning Tutorial (Data Science)

  • Week 6

    • Students must
      Mark as done

      Session III: Data Processing

      Aim of the Session:

      –  Introduce NumPy and Panda
      –  Hands on work with data using NumPy or Panda

    • url icon
      Class Video 6 (Numpy) URL
    • url icon
      Resource Link (Numpy) URL
      Students must
      Mark as done
    • Students must
      Mark as done

      Additional Links

    • Students must
      Mark as done
      Numpy Tutorial

         
    • Students must
      Mark as done

      Data Visualization

      Aim of the Session:

      –  Introduce data visualization libraries
      –  Hands on work for data visualization using different datasets


    • url icon
      Class Video 6 (Pandas) URL
    • url icon
      Class Video on Pandas (24-3-2021) URL
    • Students must
      Mark as done

      Additional Links

    • url icon
      Resource Link 1 (pandas) URL
      Students must
      Mark as done
    • url icon
      Resource Link 2 (pandas) URL
      Students must
      Mark as done
    • url icon
      Resource Link 3 (pandas) URL
      Students must
      Mark as done
    • url icon
      Resource Link 4 (pandas) URL
      Students must
      Mark as done
    • url icon
      Resource Link 5 (pandas) URL
      Students must
      Mark as done
    • page icon
      Pandas Video Tutorials Page
      Students must
      Mark as done
    • Students must
      Mark as done

  • Home Task (ML)

    Machine Learning A-Z™ Hands-On Python & R In Data Science


  • Week 8

    Session I: Machine Learning Algorithm (Linear and Logistic Regression)

    Aim of this lecture:

    –  Discuss on Linear Regression
    –  Discuss on Logistic Regression
    –  Apply Linear and Logistic Regression on Datasets  

    Session II: Machine Learning Algorithm (Support Vector Machine)

    Aim of this lecture:

    –  Discussion on SVM
    –  Apply SVM on Datasets 


    • url icon
      Machine Learning with Python Tutorial URL
      Students must
      Mark as done
    • url icon
      Class Video on ML Algorithm (25-3-2021) URL
    • url icon
      Class Video on ML Algorithm - Regressiom (31-3-2021) URL
    • Students must
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      Machine Learning With Python

  • Week 9

    Session III: Machine Learning Algorithm (KNN algorithm)

    Aim of this lecture:

    –  Discussion on KNN
    –  Apply KNN on Dataset

    Session IV: Machine Learning Algorithm (Naive Bayes)

    Aim of this lecture:

    –  Discussion on Baive Bayes
    –  Apply Naive Bayes on Datasets


    • Students must
      Mark as done

      Additional Link:

    • url icon
      Machine Learning URL
      Students must
      Mark as done
    • url icon
      Class Video on ML Algorithm 2 (26-3-2021) URL
    • url icon
      Class Video on ML Algorithm 2 (26-3-2021) URL
    • url icon
      Class Video on ML Algorithm - Classification (7-4-2021) URL

  • Week 13

    Assignment

    • assign icon
      Assignment on Python

      Submit your Kaggle notebook link here

    • assign icon
      Assignment on Python

      Submit your Kaggle notebook link here 

    • assign icon
      Assignment on Numpy and Pandas
    • assign icon
      Assignment on Numpy and Pandas

  • Lab Test

    Labtest

    • url icon
      Lab Test (K+L) URL
      Students must
      Mark as done
    • assign icon
      Lab Test Assignment
    • url icon
      Lab Test (M+N) URL
    • url icon
      Submission Link (Labtest M+N) URL

  • Week 12


    • Students must
      Mark as done

      Final Project Report

      1. Submit your project report (Introduction, Dataset description, Algorithm description, Code, Conclusion )



    • assign icon
      Submit your project and project report here Assignment
    • assign icon
      Submit your project and project report here Assignment

  • Week 11


    • url icon
      Quiz (M) URL
      Not available unless: You belong to M
    • url icon
      Quiz (N) URL
      Not available unless: You belong to N
    • url icon
      Quiz (K) URL
      Not available unless: You belong to K
    • url icon
      Quiz (L) URL
      Not available unless: You belong to L

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