The decision to use AI/ML is not just technical. It is a decision that, if planned for and responsibly adopted, can be transformational to a company’s stakeholders, workforce, and long-term business trajectory. The marketing hype is in full force and it is incumbent upon company leaders to learn the key terminology, relevant use cases, and how AI differs from previous analytic tools. While developing implementation strategies, understanding of the skills needed, development approaches, and characteristics of a successful AI implementation will help managers and executives navigate the landscape of available AI solutions, machine learning frameworks, and the large ecosystem of AI tools.
This course will help professionals develop a fundamental understanding of machine learning and derive practical solutions using predictive analytics. It introduces the concepts related to Supervised and Unsupervised machine learning from basic regression and classification to decision trees and clustering. The course will make use of Python for the hands-on implementation of the models.
- Introduction of Numpy, pandas , Matlpot lib
- Deep Learning
- Hands-on Practise
- Use cases
- Supervised and Unsupervised Machine Learning
- Overview of Python
- Data analytics
- Use Cases
- Hands-on Practical
We recommend that all students have:
- A basic understanding matrix vector operations and notation
- A basic knowledge of statistics
Hardware / PC capabilities:
A workstation with the following capabilities:
- A web browser (Chrome/Firefox)
- Internet connection
- A firewall allowing outgoing connections on TCP ports 80 and 443
- The following developer utilities should be installed:
- Jupyter Notebook (will be installed using Anaconda)
- All libraries will be installed using Anaconda
- All code will be written in Python with the use of the following libraries: Pandas/NumPy are the libraries for matrix calculations and data frame operations.
- We strongly recommend to browse through the available tutorials for these packages, for instance, the official one scikit-learn, Matplotlib
- If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.
Online Classroom preferred
- Everything in self-paced, plus
- 322 Hrs of Instructor-led Training
- 1:1 Doubt Resolution Sessions
- Attend as many batches for Lifetime
- Flexible Schedule