Ranked #1 Masters in Artificial Intelligence Program by Career Karma

You will be able to demonstrate the following ability after completing this Masters in Artificial Intelligence:

• Learn Artificial Intelligence's concept, purpose, domain breadth, phases, implementations, and impacts.
• Create real-world projects, games, prediction models, logic constraint satisfaction concerns, experience and understanding systems, probabilistic models, and agent decision-making skills using your artificial intelligent machines and models.
• Learn basic programming features and technicalities, such as data types, tuples, lists, arrays, basic operators, and functions.
• Learn how to develop your Python programs and use the Jupyter notebook to analyze fundamental data.
• Learn about data wrangling, data exploration, data visualization, hypothesis creation, and testing procedures in Data Science.
• Use the NumPy and SciPy packages for high-level mathematical and technical computing and the Pandas package for data analysis.
• Learn about supervised and unsupervised learning methods such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline, recommendation engines, and time series modeling.
• Understand TensorFlow's core principles, functions, operations, and execution pipeline.
• CNN, artificial neural networks, training deep RNN networks, and high-level NLP interfaces are among the advanced issues in AI.

Simplilearn and IBM collaborated on a Masters in AI that combines AI, Data Science, Machine Learning, and Deep Learning, allowing for the real-world application of sophisticated tools and models. This AI Masters Program will teach you the principles of statistics for machine learning, python programming, data visualization, and feature engineering. These courses will teach you how to use Python libraries like TensorFlow, Matplotlib, and Scikit-learn, as well as essential Machine Learning techniques like supervised and unsupervised learning and advanced concepts like artificial neural networks, layers of data, and feature extraction and TensorFlow.

Artificial intelligence and machine learning will substantially impact all aspects of daily life in the near future, with applications in healthcare, aviation, finance, logistics, and customer support. A job in AI puts you on the fast road to a dynamic, ever-changing industry that is predicted to grow significantly and beyond once you complete the training.

The present and future demands are enormous. According to Paysa, licensed AI Engineers make an average income of $172,000 per year in the United States (approximately Rs.17 lakhs to Rs.25 lakhs in India), with less than 10,000 competent persons in the globe to fill these roles.

Over 15 real-life projects in various areas are included in this Masters in AI co-developed with IBM. These projects are intended to help you grasp essential AI topics like supervised and unsupervised learning, reinforcement learning, support vector machines, deep learning, TensorFlow, neural networks, convolutional neural networks, and recurrent neural networks.

This training includes a capstone assignment allowing you to review the principles you've learned. You'll take specialized guided classes to develop a high-quality industrial project that addresses a real-world problem. This AI Masters Program capstone project will cover everything from exploratory data analysis to model construction and fitting. You will employ cutting-edge AI-based supervised and unsupervised algorithms such as Regression, Multinomial Naive Bayes, SVM, Tree-based algorithms, and NLP in the topic of your choice to complete this capstone project. You will receive a capstone credential for completing the project. Still, you will also have a project to present to future employers as evidence of your learning from this course.

Project 1

Socials

To reduce social hatred and negativity, create a model that employs natural language processing and machine learning to recognize inappropriate tweets that should be removed from the Twitter site.
 

Project 2

E-commerce

Amazon prime videos' movie reviews are included in the data collection. Analyze the Amazon customer movie reviews data set and create a Machine Learning recommendation system that assigns scores to each user.
 

Project 3

Automobile

Mercedes wants to reduce the time spent on its test bench to reduce its time to bring a product (car) to market. Create and optimize a Machine Learning algorithm to solve this challenge.
 

Project 4

Retail

Create a predictive model to predict sales for Walmart stores while considering special discount events. Examine how sales are affected by macroeconomic variables such as the CPI and the unemployment rate as features.
 

Project 5

Telecommunications

Comcast intends to increase customer happiness by identifying and addressing issue areas and is looking for solutions that might be implemented.
 

Project 6

E-commerce

ML engineers must analyze Amazon user evaluations of various items based on the data set supplied and forecast sentiment or satisfaction based on feature or review content.
 

Project 7

Commerce

The banking industry is the most prevalent employer of data scientists. Scammers that try to fool the system are continuously targeting it. Credit card businesses must be able to discover illicit credit card frauds, notwithstanding the difficulty of precisely recognizing fraudulent and illegal activities. Several methodologies, such as classification with overfitting, unsupervised detection approaches, and heuristics, must be used to achieve maximum accuracy in detecting fraud.
 

Project 8

Retail

The most significant aspect of managing the retail supply chain is demand forecasting. Professionals must grasp Data Science and ensemble methodologies to do so efficiently. For the following month, you must forecast daily sales for each store.

Simplilearn's Artificial Intelligence Masters Program is well-suited for several vocations and professions, such as:

• Individuals with an interest in working as an AI or Machine Learning Engineer.
• Analytical managers supervise a group of analysts.
• Data architects who want to learn about artificial intelligence systems and algorithms.
• Data analysts are interested in working in machine learning or artificial intelligence.
• Professionals were interested in artificial intelligence or machine learning as a profession.
• Experts wish to improve their understanding of their fields by using Artificial Intelligence.