Course in Data Science (DS) and Machine Learning (ML)

Duration

40 Hours

Enroll

About the Course

Data Science and Machine learning are gaining importance in today’s era as data from various fields or industries is increasing tremendously. It has shown its significance in Medical, Agriculture, Commerce, Science and technology.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured
and unstructured data. Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data. Both streams are realised as a part of Artificial Intelligence.
The platform introduce through this course, to get acquaint with these technologies with hands on experience and real time applications.

Your Instructor

Vaibhav Bachuwar

Learning Objectives

Our main objective is to impart knowledge to passionate students/researchers about:
1. Data Science and Big data
2. Machine Learning algorithms and its application
3. Introduction to AI

Learning Outcomes

Upon the completion of the course students will be able to;
- Use concepts of Data Science
- Apply different data science tools for data analytics and programming
- Deploy algorithms for Machine Learning for data science
- Design and development these techniques for Specific Applications
The module helps to enhance the career in Data Science and Machine Learning domain.

Prerequisites

Minimum eligibility criteria to enroll in this course-
Pursuing / Passed B. A, BBA, B. Com, M. A, MBA, M. Com / B.E, B. Tech / BCA, MCA / B. Sc, M. Sc /Polytechnic Diploma in the field of Electrical/Electronics/Instrumentation/Biomedical/Compute Science/Information Technology This course is intended for enthusiastic students having basic knowledge of Electronics domain, here we assume that candidate is already familiar with the basics of Data.

Syllabus

1. Introduction to Data Science
- Understanding the Statistical terms- Population, Sample points, Parameter Vs Statistics,
- Statistic parameters- Mean, Median, Mode, Measure of variation, Range, Variance, SD, Quantiles, P Value, Errors, etc.
- Overview of Big Data
- Data Science, Artificial Neural Network, Machine Learning and Deep Learning
2. Introduction to Tools for Data Science
- Introduction to Different tools for DS Excel, Python, MATLAB
- Excel for Data Science
- Implementation of parameters in excel
- Case studies using Excel
- Introduction to Python
- Python Programming
- Jupyter Notebook, Numpy, Matplotlib
- Introduction to MATLAB
- Matlab Environment
- Matlab Tools
- Curve Fitting and ANN
3. Machine Learning-Techniques
- Supervised Learning
- Classification using- SVM, Discriminant Analysis, K-Nearest neighbor
- Regression- Linear regression, Logistic Regression
- Unsupervised Learning
- Clustering- K-Means, ANN
4. Applications of Machine learning in Data Science
- Case Study in Finance/ Marketing/ Data Security/ Any Other 5. Overview to Deep learning
- Concept of Deep learning and employ for Image classification / Speech recognition
5. Applications of AI
 Applications of AI in various sectors

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