Designation: Lecturer
Email: saifulbadhon.sb3@gmail.com
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
Class Attendance
10
Lab Performance
25
Project
Lab Final
40
Total
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
data access: https://drive.google.com/drive/folders/1-TR3w1o_D55Uj1iSY8a7o5zEBGW08ThS?usp=sharing
Session I: Basics of Python
Intended Learning Outcome: At the end of the session, student should be able to:
• Give some simple examples of Prolog programs
• Discuss the three basics of Python
– Syntax
– Variables and data types
– List, tuples, dictionaries
- Conditions and Loops
Session II: Basics of Python
Aim of this leacture:
• The following topics will be discussed in the class
– Condition and Loops
- Function
- Class and Objects
Session III: Data Processing
Aim of the Session:
– Introduce NumPy and Panda– Hands on work with data using NumPy or Panda
Session IV: Data Visualization
– Introduce data visualization libraries– Hands on work for data visualization using different datasets
Session V: 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 VI: Machine Learning Algorithm (KNN algorithm)
– Discussion on KNN– Apply KNN on Datasets
Midterm Exam
date and time will announced later.
Session VII: Machine Learning Algorithm (Naive Bayes)
– Discussion on Baive Bayes– Apply Naive Bayes on Datasets
Session VIII: Machine Learning Algorithm (Support Vector Machine)
– Discussion on SVM– Apply SVM on Datasets
Session IX: Machine Learning Algorithm (Neural Network)
– Discussion on Neural Network– Implementation of Neural Network on Datasets
Session X: Machine Learning Algorithm (Neural Network - 2)
Project Presentation
Final Exam