(18CS146) MACHINE LEARNING LAB
Year
|
Semester
|
Hours/Week
|
C
|
Marks
|
L
|
T
|
P/D
|
CIE
|
SEE
|
Total
|
IV
|
I
|
-
|
-
|
2
|
1
|
30
|
70
|
100
|
Pre-requisite
|
Nil
|
COURSE OUTCOMES
At the end of the course the student will able to
Analyse the learning techniques with this basic knowledge.
Apply effectively neural networks and genetic algorithms for appropriate applications.
Use bayesian techniques and derive effectively learning rules.
Determine which learning techniques are appropriate to a particular problem domain
Evaluate different machine learning techniques (e.g., robustness, sensitivity, specificity, advantages, limitations, etc.) by comparing and assessing their computational results
LIST OF EXPERIMENTS
Python Basics: Your first program, Types, Expressions and Variables, String Operations, Python Data Structures: Lists and Tuples, Sets, Dictionaries
Python Programming Fundamental: Conditions and Branching, Loops, Functions Objects and Classes. Working with Data in Python: Reading files with open, writing files with open, loading data with Pandas, Working with Pandas and numpy.
Regression: – Introduction, Features and Labels, Training and Testing, Forecasting and Predicting, How to program the Best Fit Slope, How to program the Best Fit Line.
Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
Building a Logistic Regression in Python, Give the input and predict the variables (desired target), Make visualizations, create dummy variables. Build an Artificial Neural Network by implementing the Back propagation algorithm and test the same using appropriate data sets. Kernels, Soft Margin SVM, and Quadratic Programming with Python
Take a data set and apply basic classification algorithm and test the accuracy using python
Support Vector Machines : Vector Basics, Support Vector Assertions, Constraint, Optimization with Support Vector Machine
Handling Non-Numerical Data for Machine Learning, K-Means with Titanic
Dataset
Make Collaborative Filtering and Recommendation for a data set
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