AI-ML

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Skill Nation

Certificate Program in Artificial Intelligence & Machine Learning

Artificial Intelligence & Machine Learning

Overview

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)

Features

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.

Key Differentiator

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.

Curriculum

Unit No.	Unit Name	Topics Covered	Learning Objectives
1	Introduction to AI	Sophia Alpha Go and the rebirth of AI Sneak peek into the future current trends in AI	AI – Introduction, current trends & future. In depth knowledge about the first human-like robot Sophia and Alpha Go.
2	Applications of AI	Enterprise Applications of AI-Industries Consumer Applications - Gaming, Home Automation	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	Understanding Artificial intelligence, Machine learning and Deep learning. Use case driven comparison of AI, ML and DL	Comprehend the difference between Machine Learning and Deep Learning using relevant use cases.
4	Introduction to ML	What is ML, Need for ML, classification of ML algorithms- supervised and unsupervised learning. Types of ML algorithms	Machine Learning- definition, need, learning approaches – supervised and unsupervised, sneak peak of different ML algorithms
5	ML Techniques	Classification and Regression – understanding classification and regression techniques with case studies	Apprehend the concepts of Classification and regression models using case studies.
6	ML with Scikit-learn	Introduction to Scikit- learn package in python. Implementing ML algorithms in python using Scikit-learn	Get an overview of Scikit-learn – a free Machine Learning library in Python
7	ML Algorithms	Linear, Logistic regression; Decision trees Support Vector Machines. Python hand on using Scikit-learn	Understand how to import different Machine Learning algorithms in Scikit-learn and build models.
8	Training and Deploying models	Creating training models using ML algorithms and deploying the models, Understand how to deploy models after training	Training and Deploying different ML models.
9	Text Analytics	Basics of text processing, lexical processing, syntax and semantics of text processing, other problems in text analytics	Text Processing – Basics, Lexical processing, their syntax and semantics and use cases.
10	Natural Language Processing	Introduction and applications of NLP. Learning the business use cases of Natural language processing. Statistical NLP and text similarity, syntax and parsing techniques, text summarization techniques, semantics and generation	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.