Certificate Program in Artificial Intelligence & Machine Learning
Artificial Intelligence (AI) and Machine learning (ML) not only improve the customer experience, but also change the way companies operate. An increasing number of technology savvy industries are recognizing the benefits of AI & ML. They are using this technology to run their organizations efficiently, serve their customer better and increase profitability. Thus, AI & ML are among the most sought skillset globally and ML Engineers have been ranked among the top emerging jobs on LinkedIn.
In this course, you will learn about various concepts in Machine Learning – its tools and algorithms. You will learn how to train and deploy ML models and go through a few AI concepts and AI applications like gaming and home automation. Programming in Python is also a skill you will pick up during the course. At the end of the course, you will get a certification from Manipal Executive Education & KHDA (Knowledge & Human Development Authority) Dubai – Permit Number - 61760, which is highly valued across industries.
Target Market – Who Should Attend
- Fresh Engineering Graduates
- IT Professionals -1 to 8 years of Experience- Software Developer, Cloud Developer, Software Analyst, Data Analyst, Business Analyst, Systems & Hardware Engineers
- Non-IT Professionals- Statisticians, Doctors- Radiologists & Surgeons (Secondary TG)
- Certificate from Manipal ProLearn, leading to a PG Certificate from Manipal Academy of Higher Education (after completion of Certificate Program in Neural Networks with Tensor Flow and Certificate Program in Deep Learning with Tensorflow)
- Hands on Practice environment with GPU-based Cloud Lab
- Industry projects, demo codes and case studies to improve practical learning
- Weekly webinar sessions led by expert instructors
- Network with AI Experts and Enthusiasts through Meetups.
- 60+ Hours of Cloud & GPU Labs for Hands-on Practice, highest in the industry. Real world applications of AI & DL involve use of Graphical Processing Unit (GPU) environments to expedite the Neural Network training process.
- 30+ Hours of Online Live Lectures by Industry Experts, 12 Weekends X 3 Hours
- 5+ Demo Code on Real Life Case Scenario across industries like Banking & Insurance, Health Care, Automobile, Sports, Travel & Retail.
Below are some of the Case Scenarios:
- Identify competitors of Mercedes using word2vec technique in Text Analytics.
- Predicting direction of Penalty kick – using EPL data
- Predicting Train ticket reservation.
- Retail price prediction
- Creating Model to classify whether a person will be a prospective customer for Insurance using the Lead data.
- Build a Machine learning algorithm to identify whether a person would get breast cancer or not, using Wisconsin Breast Cancer dataset.
|Unit Name||Topics Covered||Learning Objectives|
|1||Introduction to AI|| ||AI – Introduction, current trends & future. In depth knowledge about the first human-like robot Sophia and Alpha Go.|
|2||Applications of AI|| ||Understand the application of AI in different verticals and also in depth about its use in consumer Applications like Gaming and Home Automation.|
|3||AI, ML and DL|| ||Comprehend the difference between Machine Learning and Deep Learning using relevant use cases.|
|4||Introduction to ML|| ||Machine Learning- definition, need, learning approaches – supervised and unsupervised, sneak peak of different ML algorithms|
|5||ML Techniques|| ||Apprehend the concepts of Classification and regression models using case studies.|
|6||ML with Scikit-learn|| ||Get an overview of Scikit-learn – a free Machine Learning library in Python|
|7||ML Algorithms|| ||Understand how to import different Machine Learning algorithms in Scikit-learn and build models.|
|8||Training and Deploying models|| ||Training and Deploying |
different ML models.
|9||Text Analytics|| ||Text Processing – Basics, Lexical processing, their syntax and semantics and use cases.|
|10||Natural Language |
| ||Natural Language Processing – |
Introduction, Applications & use cases.
Get an overview of important concepts like Statistical NLP, text similarity, syntax and parsing techniques & text summarization techniques.