Course in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)

Duration

40 Hours

Enroll

About the Course

Artificial Intelligence, Machine learning and Deep Learning are upcoming technologies which has covered almost each and every field. It has shown its significance in Medical, Agriculture, Commerce, Science and technology. ML & DL are the study of computer algorithms that can improve automatically through experience and by the use of data. Both streams are realized as a part of Artificial Intelligence.
When exposed to a large data set, the machine can detect patterns and use historical and real-time data to determine the best course of action or procedure that will deliver the best result in the shortest possible time.
We introduce the platform to get acquainted with such technologies with hands on experience and real time applications.

Your Instructor

Vaibhav Bachuwar

Santosh is the founder and director of DesiColab, a social venture empowering young innovators, change-makers in India.

Santosh belongs to an underprivileged community where it was highly difficult for him to get access to quality education and supportive learning environment. In spite of all challenging situations Santosh perused his education in engineering. He further earned his Master’s degree in Design from IDC, IIT Mumbai. During 2009 Santosh was awarded with scholarship of government of Maharashtra for pursuing higher education abroad. He went to Milan, Italy, for his second Masters in Strategic Design.

Learning Objectives

Main objective of the certification course is to impart knowledge to passionate students about:
- Artificial Intelligence and its Applications
- Machine Learning algorithms and its applications
- Deep Learning and its applications
- Data Science and Data Analysis
- Overall understanding of how artificial intelligence (AI) led to machine learning (ML), which then led to deep learning (DL). The module helps to enhance the career in Data Science domain.

Learning Outcomes

Upon completion of the course students will be able to;
- Differentiate between Neural networks, Machine Learning and Deep Learning.
- Deploy algorithms for Machine Learning
- Develop an application based on ML & DL
- Use concepts of Data Science and deep learning
- Design and development these techniques for Specific Applications

Prerequisites

Minimum eligibility criteria to enroll in this course-
Pursuing / Passed BE / B. Tech / MCA / BCA / BSc / MSc /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 Science and basic understanding of electronics and statistics is also expected.

Syllabus

1. Introduction to AI, ML & DL
- Overview of AI, ML & DL
- Application of AI, ML& DL in different Fields
- Difference between Neural network, Machine learning and Deep learning
- Data Processing- Data Types, Data Analysis, Data Imputation
- Applied Statistics- Probability, Conditional Probability and Probability Distributions, Hypothesis Testing. Inferential Statistics

2. Tools for Machine Learning
- Different tools for ML
- MATLAB environment-
- Python environment- Python Functions & Packages, Working with Data Structures, Arrays, Vectors & Data Frames Jupyter Notebook, Pandas, NumPy, Matplotlib,
- Introduction to other languages like- Octave, R Programming

3. Machine Learning-Techniques
- Supervised Learning
- Classification using- SVM, Discriminant Analysis, K-Nearest neighbor
- Regression- Linear regression, Logistic Regression
- Unsupervised Learning
- Clustering- K-Means, HMM & ANN
- Implementation of these algorithms in MATLAB / Python

4. Introduction to Deep Learning
- Overview of Deep Learning
- Difference between Machine Learning & Deep Learning
- Deep learning Algorithms
- Applications of deep learning

5. Introduction to DataScience
- What is data science?
- TensorFlow & Keras for Neural Networks
Application of data Science
- Application of ML, DL and DS in AI

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