Artificial Intelligence
About the Program
The AI Engineer Masters Program, in collaboration with JUST, covers the crucial skills you need for a successful career in Artificial Intelligence (AI). As you undertake this Artificial Intelligence program, you master the concepts of Machine Learning and Deep Learning along with the internationally-acclaimed programming language Python needed to excel in the field of AI. You will also learn how to design intelligent models and advanced artificial neural networks, and leverage predictive analytics to solve real-time problems to take your career in Artificial Intelligence to the next level.
Program Outcomes
- Learn about the major applications of Artificial Intelligence across various use cases across various fields like customer service, financial services, healthcare, etc.
- Master the skills and tools used by the most innovative Artificial Intelligence teams across the globe as you delve into specializations, and gain experience solving real-world challenges.
- Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, and tracking.
- Design and build your own intelligent agents and apply them to create practical Artificial Intelligence projects including games, Machine Learning models, logic constraint satisfaction problems, knowledge based systems, probabilistic models, agent decision-making functions and more.
- Gain the ability to apply Artificial Intelligence techniques for problem solving and explain the limitations of current Artificial Intelligence techniques.
- Understand the concepts of
Tensor Flow, its main functions,
operations, and the execution
pipeline
- Learn to deploy deep learning
models on Docker, Kubernetes, and
in server less environments (cloud)
- Understand and master the concepts
and principles of Machine Learning,
including its mathematical and
heuristic aspects
- Understand the fundamentals of
Natural Language Processing using
the most popular library; Python Natural Language Toolkit (NLTK).
- Master and comprehend advanced
topics such as convolutional neural
networks, recurrent neural networks,
training deep networks, and high level interfaces.
Who Should Enroll in this Program?
With the demand for Artificial
Intelligence in a broad range of
industries such as banking and
finance, manufacturing, transport
and logistics, healthcare, home
maintenance, and customer
service, the Artificial Intelligence
course is well suited for a variety
of profiles like:
1.Developers aspiring to be an Artificial Intelligence Engineer or Machine Learning engineers
2.Analytics managers who are
leading a team of analysts
3.Information architects who
want to gain expertise
in Artificial Intelligence
algorithms
4.Graduates looking to build a
career in Artificial Intelligence
and Machine Learning
Introduction to Artificial Intelligence
Simple learn Introduction to Artificial Intelligence course is designed to help learners decode the mystery of Artificial Intelligence and understand its business applications. The course provides an overview of Artificial Intelligence concepts and workflows, Machine Learning, Deep Learning, and performance metrics. You learn the difference between supervised, unsupervised learning be exposed to use cases, and see how clustering and classification algorithms help identify Artificial Intelligence business applications.
Key Learning Objectives
- Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
- Fundamental concepts of Machine Learning and Deep Learning
- Difference between supervised, semi-supervised and unsupervised learning
- Machine Learning workflow and how to implement the steps effectively
- The role of performance metrics and how to identify their essential methods
Course curriculum
- Lesson 1 - Decoding Artificial Intelligence
- Lesson 2 - Fundamentals of Machine Learning and Deep Learning
- Lesson 3 - Machine Learning Workflow
- Lesson 4 - Performance Metrics
Data Science with Python
This Data Science with Python course will establish your mastery of
Data Science and analytics techniques using Python. With this Python
for Data Science Course, you learn the essential concepts of Python
programming and gain in-depth knowledge in data analytics, Machine
Learning, data visualization, web scraping, and natural language
processing. Python is a required skill for many Data Science positions,
so jump start your career with this interactive, hands-on course.
Key Learning Objectives
- Gain an in-depth understanding of Data Science processes, data
wrangling, data exploration, data visualization, hypothesis building,
and testing. You will also learn the basics of statistics
- Install the required Python environment and other auxiliary tools and
libraries
- Understand the essential concepts of Python programming such as
data types, tuples, lists, dicts, basic operators and functions
- Perform high-level mathematical computing using the NumPy package
and its vast library of mathematical functions
- Perform scientific and technical computing using the SciPy package
and its sub-packages such as Integrate, Optimize, Statistics, IO, and
Weave
- Perform data analysis and manipulation using data structures and
tools provided in the Pandas package
- Gain expertise in Machine Learning using the Scikit-Learn package
- Gain an in-depth understanding of supervised learning and
unsupervised learning models such as linear regression, logistic
regression, clustering, dimensionality reduction, K-NN and pipeline
- Use the Scikit-Learn package for natural language processing
- Use the matplotlib library of Python for data visualization
- Extract useful data from websites by performing web scraping using
Python
- Integrate Python with Hadoop, Spark, and MapReduce
Course curriculum
Lesson 1: Data Science Overview
Lesson 2: Data Analytics Overview
Lesson 3: Statistical Analysis and Business Applications
Lesson 4: Python Environment Setup and Essentials
Lesson 5: Mathematical Computing with Python (NumPy)
Lesson 6 - Scientific computing with Python (Scipy)
Lesson 7 - Data Manipulation with Pandas
Lesson 8 - Machine Learning with Scikit Learn
Lesson 9 - Natural Language Processing with Scikit Learn
Lesson 10 - Data Visualization in Python using matplotlib
Lesson 11 - Web Scraping with Beautiful Soup
Lesson 12 - Python integration with Hadoop MapReduce and Spark
Machine Learning
Simplilearns Machine Learning course will make you an expert in Machine
Learning, a form of Artificial Intelligence that automates data analysis to
enable computers to learn and adapt through experience to do specific
tasks without explicit programming. You will master Machine Learning
concepts and techniques, including supervised and unsupervised learning,
mathematical and heuristic aspects, and hands-on modeling to develop
algorithms and prepare you for your role with advanced Machine Learning
knowledge.
Key Learning Objectives
- Master the concepts of supervised and unsupervised learning,
recommendation engine, and time series modeling
- Gain practical mastery over principles, algorithms, and applications of
Machine Learning through a hands-on approach that includes working
on four major end-to-end projects and 25+ hands-on exercises
- Acquire thorough knowledge of the statistical and heuristic aspects of
Machine Learning
- Implement models such as support vector machines, kernel SVM,
naive Bayes, decision tree classifier, random forest classifier, logistic
regression, K-means clustering and more in Python
- Validate Machine Learning models and decode various accuracy
metrics. Improve the final models using another set of optimization
algorithms, which include Boosting & Bagging techniques
- Comprehend the theoretical concepts and how they relate to the
practical aspects of Machine Learning
Course curriculum
Lesson 1: Introduction to Artificial Intelligence and Machine Learning
Lesson 2: Data Preprocessing
Lesson 3:Supervised Learning
Lesson 4: Feature Engineering
Lesson5:SupervisedLearning-Classification
Lesson 6: Unsupervised learning
Lesson 7: Time Series Modelling
Lesson 8: Ensemble Learning
Lesson 9:Recommender Systems
Lesson 10: Text Mining
Deep learning with Keras and Tensor Flow
This Deep Learning with Tensor Flow course by IBM will refine your
machine learning knowledge and make you an expert in deep learning
using Tensor Flow. Master the concepts of deep learning and Tensor Flow
to build artificial neural networks and traverse layers of data abstraction.
This course will help you learn to unlock the power of data and prepare
you for new horizons in AI.
Key Learning Objectives
- Understand the difference between linear and non-linear regression
- Comprehend convolutional neural networks and their applications
- Gain familiarity with recurrent neural networks (RNN) and
autoencoders
- Learn how to filter with a restricted Boltzmann machine (RBM)
Course curriculum
Lesson 1 - Introduction to Tensor Flow
Lesson 2 Convolutional Neural Networks (CNN)
Lesson 3 Recurrent Neural Networks (RNN)
Lesson 4 -Unsupervised Learning
Lesson 5 - Autoencoders
Artificial Intelligence Learning Objectives
Our Artificial Intelligence course will allow you to implement the skills
you learned in the masters of Artificial Intelligence. With dedicated
mentoring sessions, you know how to solve a real industry-aligned
problem. You learn various Artificial Intelligence-based supervised and
unsupervised techniques like Regression, SVM, Tree-based algorithms,
NLP, etc. The project is the final step in the learning path and will help you
to showcase your expertise to employers.
Key Learning Objectives
Our online Artificial Intelligence Capstone course will bring you through
the Artificial Intelligence decision cycle, including Exploratory Data
Analysis, building and fine-tuning a model with cutting edge Artificial
Intelligence-based algorithms and representing results. The project
milestones are as follows:
- Exploratory Data Analysis - In this step ,you will apply various data
processing techniques to determine the features and correlation
between them, transformations required to make the data sense,
new features, construction, etc.
- Model Building and fitting - This will be performed using Machine
Learning algorithms like regression, multinomial Nave Bayes, SVM,
tree-based algorithms, etc.
- Unsupervised learning - Clustering to group similar kind of
transactions/reviews using NLP and related techniques to devise
meaningful conclusions.
Python for Data Science
Kickstart your learning of Python for Data Science with this introductory
course, carefully crafted by IBM. Upon completion of this course, you will
be able to write Python scripts and perform fundamental, hands-on data
analysis using the Jupyter-based lab environment..
Key Learning Objectives
Hr venture online Python for Data Science course will bring you
- Write your first Python program by implementing concepts of
variables, strings, functions, loops, and conditions
- Understand the nuances of lists, sets, dictionaries, conditions,
branching, objects, and classes
- Work with data in Python, such as loading, working, and saving data
with Pandas, and reading and writing files
Topics Covered:
- Python Basics
- Python Data Structures
- Python Programming Fundamentals
- Working with Data in Python
- Working with NumPy Arrays
Advanced Deep Learning and
Computer Vision
Take the next big step toward advancing your Deep Learning skills with
this high-level course. This Advanced Deep Learning and Computer
Vision course includes Computer Vision Basics with Python; Advanced
Computer Vision with OpenCV 4, Keras, and Tensor Flow 2; Computer
Vision for OCR and Object Detection; and Py Torch for Deep Learning and
Computer Vision to ensure you are prepared for your Deep Learning and
computer vision journey.
Key Learning Objectives
- Understand 2D Scaling Transformations, 2D Geometric
Transformations, Binary Morphology, Image Filtering, and Shape
Detection through Transform
- Implement Object Detection, YOLO, Object Tracking, Motion, 3D
Reconstruction, and Smart CCTV Project
- Computer vision with OpenCV, Image Manipulation in OpenCV
Operations, Image Segmentation, and ML and DL on computer vision
- Introduction to OCR, Tesseract Image OCR Implementation
- DNN - PyTorch, Linear Regression; PyTorch, Image Recognition;
PyTorch, CNN; PyTorch, CIFAR 10 Classification; PyTorch, Transfer
Learning - Pytorch
Topics Covered:
- Computer Vision Basics withPython
- Advanced Computer Vision with OpenCV 4, Keras, and TensorFlow 2
- Computer Vision for OCR and Object Detection
- PyTorch for Deep Learning and Computer Vision
Natural Language Processing and
Speech Recognition
This Natural Language Processing and Speech Recognition course will
give you a detailed look at the science of applying Machine Learning
algorithms to process large amounts of natural language data. This
module focuses on natural language understanding, feature
engineering, natural language generation, automated speech recognition,
speech-to-text conversion, text-to-speech conversion, and voice
assistance devices.
Key Learning Objectives
- Understand the concepts, tools, and techniques of NLP
- Learn about natural language understanding and natural language
generation
- Perform text mining
- Extract intent and entities
- Understand the vector space model
- Apply vector, matrix, and algebra on data
- Learn about feature engineering
- Understand the syntactic and semantic structure of a sentence
- Hands-on experience with Python libraries
- How to apply Machine Learning and Deep Learning with NLP
Topics Covered:
- Introduction to Natural Language Processing
- Feature Engineering on Text Data
- Natural Language Understanding Techniques
- Natural Language Generation
- Natural Language Processing Libraries
- Natural Language Processing with Machine Learning and
- Deep Learning
- Introduction of Speech Recognition
- Signal Processing and Speech Recognition Models
- Speech-to-Text
Industry Master Class Artificial
Intelligence
Attend this online interactive industry masterclass to gain insights about
advancements in Data Science, AI, and Machine Learning techniques
Course Price :
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