Machine Learning Lab
The Machine Learning Lab provides practical exposure to the concepts and techniques used in designing intelligent systems. It focuses on implementing algorithms that enable computers to learn from data and make predictions or decisions without being explicitly programmed.
In this lab, students work with various supervised and unsupervised learning algorithms such as linear regression, decision trees, support vector machines, and clustering techniques. The lab emphasizes hands-on experience using programming tools like Python along with libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. Overall, the Machine Learning Lab helps students bridge the gap between theoretical knowledge and practical implementation, preparing them for applications in fields such as healthcare, finance, automation, and artificial intelligence.
Course Objectives
| Code |
Objective |
| C.OB-1 |
To understand the need of machine learning |
| C.OB2 |
To learn about regression and feature selection. |
| C.OB3 |
To understand about classification algorithms. |
| C.OB4 |
To learn clustering algorithms |
Course Outcomes
| CO |
Statement |
Blooms Level |
| ML-407P.1 |
To formulate machine learning problems |
Remember, Analyze |
| ML-407P.2 |
Learn about regression and feature selection techniques and develop applications based on the same. |
Remember, Understand, Apply, Evaluate, Create |
| ML-407P.3 |
Apply machine learning techniques such as classification to practical applications. |
Understand, Remember, Apply, Analyze |
| ML-407P.4 |
Apply Clustering algorithms to develop various practical applications. |
Understand, Remember, Apply, Create |
Note: Enter correlation levels 1, 2 or 3 as defined below:
1: Slight (Low) 2: Moderate (Medium) 3: Substantial (High)
Correlation Table
| CO |
PO 1 |
PO 2 |
PO 3 |
PO 4 |
PO 5 |
PO 6 |
PO 7 |
PO 8 |
PO 9 |
PO 10 |
PO 11 |
PO 12 |
PSO 1 |
PSO 2 |
| CO1 |
3 |
3 |
3 |
3 |
3 |
2 |
2 |
– |
– |
– |
– |
2 |
2 |
2 |
| CO2 |
3 |
3 |
3 |
3 |
3 |
2 |
2 |
– |
– |
– |
– |
2 |
2 |
2 |
| CO3 |
3 |
3 |
3 |
3 |
3 |
2 |
2 |
– |
– |
– |
– |
2 |
2 |
2 |
| CO4 |
3 |
3 |
3 |
3 |
3 |
2 |
2 |
– |
– |
– |
– |
2 |
2 |
2 |
Facilities
Operating System / Software
| Sr. No. |
Name |
Version |
| 1 |
Windows |
20 |
| 2 |
Python Anaconda |
Open |
Hardware Specifications
| S.No. |
Equipment Name |
Specification |
Count |
| 1 |
Computer System |
Intel Core i9 10 Gen 2.10 GHz 32 GB RAM |
1 |
| 2 |
Computer System |
Intel Core i5 4 Gen 2.10 GHz 16 GB RAM |
2 |
| 3 |
Computer System |
Intel Core i7 8 Gen 3.10 GHz 8 GB RAM |
2 |
| 4 |
Computer System |
Intel Core i5 10 Gen 2.10 GHz 8 GB RAM |
27 |
| 5 |
Computer System |
GPU System |
1 |
| 6 |
Printer |
HP Laser Jet Pro P1108 |
1 |
| 7 |
ZYBO Board |
Zybo + Accessory Kit (410-279) |
2 |
| 8 |
ZED Board |
Zed Board (410-248) |
1 |
| 9 |
GPU SYSTEM |
Intel Xeon W-2245 RTX 2080 Ti GPU |
1 |
Staff
- Lab Incharge: Dr Ruchi Sharma
- Lab Assistant: Mr. Deepanshu
Click for the Lab e-Content: (Link for Lab Manual)
