MACHINE LEARNING
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Contents |
Course Details
| Course Code | L | T | P | C |
|---|---|---|---|---|
| SB8008 | 1 | 0 | 2 | 2 |
COURSE OBJECTIVE
The objective of this course is to provide a view of data science, recognize why data science is gaining importance in today’s business world to comprehend where data science can be applied across industry domains to understand major components of data science stack to learn how a data science project is implemented step-by-step in each business use-case
Pre-requisite courses
| Pre-requisite Knowledge | Courses Available on Springboard |
|---|---|
| Probability and Statistics | Probability and Statistics Probabilty distribution using Python Statistical Interence using Python |
| Python Programming Language | Programming Fundamentals using Python - Part 1 |
| Linear Algebra | Basics of Linear Algebra |
| Regression Analysis | Regression Analysis |
SYLLABUS
UNIT I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Why AI? - What is AI? - AI in Practice - AI in Business - AI Platforms.
UNIT II INTRODUCTION TO DATA SCIENCE
Data Science: The Data Revolution - Components of Data Science - Data Science in Action – Conclusion.
UNIT III - PYTHON FOR DATA SCIENCE
Why Python Libraries – NumPy - Introduction to NumPy - Operations on NumPy – Pandas – Introduction to Pandas – Introduction to Pandas Object – Working with datasets – Pandas Plots - Matplotlib – Introduction to Matplotlib – Types of Plots – Scikit-learn – Machine Learning using sklearn. [Practical hands-on exercises using NumPy, Pandas, Matplotlib]
UNIT IV - DATA VISUALIZATION USING PYTHON
Data visualization using Python: Data Visualization: Developing insights from data using Basic Plots using Matplotlib (Box, Scatter, Line, Bar, Pie, Histogram), Statistical analysis using Heatmap, Kernel Density plot using Seaborn, Network Graphs, Choropleth Map Using Ploty, Word Cloud. [Practical hands-on exercises for creating charts]
UNIT V - EXPLORE MACHINE LEARNING USING PYTHON
Introduction to Machine Learning - Regression – Classification – Clustering – Introduction to Artificial Neural Network. [Hands-on Exercises for Practicing Machine Learning Models Using Capstone Project]
TOTAL: 45 PERIODS
SUGGESTED ACTIVITIES
- Continuous / Self-Assessment (MCQ)
- Capstone Project – Build a ML model using a given numerical COVID’19 dataset, predict the number of confirmed cases for next ten days in different areas of the world
SUGGESTED EVALUATION METHODS
- Video Proctored Exam
- Self-Assessment
COURSE OUTCOMES
On completion of the course, students will be able to:
CO1 :Demonstrate fundamental understanding of the history of artificial intelligence (AI)and its foundations.
CO2 :Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.
CO3 :Assess and select appropriate data analysis models for solving real-world problem.
CO4 :Demonstrate the importance of data visualization, design, and use of visual components.
REFERENCE(Course Material)
- Introduction to AI
- Introduction to Data Science
- Python for Data Science
- Data visualization using Python
- Explore Machine Learning
- Object Detection and Recognition Using Deep Learning in OpenCV
Mode of Training: Online (Self-Learning)
Software Configuration to be arranged in Institution Premises: o Python and related libraries
Hardware Configuration to be arranged in Instituion Premises: o Windows 10, 16GB RAM
Course Evaluation: Online Assessment
Multiple Hybrid Branch of Students Applicable for Mechanical/Chemical
Internship/Placement Opportunities Click Here
NOS Alignment: Yes, Infosys Industry Standard
Train-the-Trainer: Faculty Enablement Program
Commercials: Free of Cost
