Masters in Artificial Intelligence and Machine Learning

BY
Careerera

Learn about NLP, deep learning, predictive analysis and various stages of machine learning and machine intelligence.

Mode

Online

Duration

12 Months

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based

Course overview

This Master of AI and Machine Learning online course is designed for people looking to change or advance their careers in this exciting and well-paying AI and ML industry. The course welcomes both professionals and newcomers who want to make an excellent career in the business. 

The Master of AI and Machine Learning online course seeks to produce highly well-rounded AI specialists who are well-versed in mathematics, proficient in suitable languages, and knowledgeable in sophisticated algorithms and applications. Because of the growing acceptance of AI & ML across industries, the demand for skilled professionals with a solid grasp of this technology has increased.

Because of the growing demand for quick and precise decision making, Machine Learning and Artificial Intelligence technologies are being adopted at a rapid pace. Careerera offers an advanced Masters in Artificial Intelligence and Machine Learning syllabus that is developed and carried out by experts with extensive experience. By enrolling in this training, students will be able to learn from the best.

The highlights

  • Job Assistance
  • Live Online classes
  • 12 Months duration
  • Industrial Projects
  • Course Completion Certificate
  • Student Handouts
  • Multiple Simulation Exams
  • Industry Based Trainers

Program offerings

  • Capstone projects
  • Videos
  • Examinations
  • Assignments
  • Online learning
  • Surprise tests
  • Mock papers
  • Notes

Course and certificate fees

certificate availability

Yes

certificate providing authority

Careerera

Who it is for

  • Data Professionals, Data administrators, Data analysts, IT Professionals, and Individuals with basic programming abilities who are interested in AI and machine learning.
  • Data Scientists expecting a significant increase in their careers.
  • Professionals seeking a career change in AI & ML.

Eligibility criteria

  • A Bachelor’s degree with a minimum of 50% marks or equivalent.
  • Basics programming language and academic level knowledge of statistics and maths.

Certification Qualifying Details

  • To qualify for the Masters in Artificial Intelligence and Machine Learning certification, candidates pass an exam conducted by Careerera at the end of the course.

What you will learn

Machine learning Knowledge of artificial intelligence

After completing the Masters in Artificial Intelligence and Machine Learning online training, candidates will learn about Natural Language Processing, Reinforcement Learning, Deep Learning, Predictive Analytics, and Statistics along with Graphical Models. Candidates will gain deep knowledge about various stages of Machine Intelligence and Machine Learning.

The syllabus

Statistical Learning

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem

Gradient Descent

  • Probability distributions
  • Hypothesis testing & scores
  • Experiential learning project

Python for AI & Machine Learning

  • Python Overview
  • Python Basics
  • Python functions, packages, and routines
  • Pandas, NumPy, Matplotlib introduction
  • Pandas for Pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Seaborn for Data Visualization
  • Sci-kit Library
  • Case Studies and careers
  • Experiential Learning project
  • Introduction to Anaconda/Jupyter for coding/data visualisation

Data Science

  • Introduction to Data Science, ML, AI

Machine Learning

Supervised Learning
  • Introduction to Machine Learning
  • Supervised Learning concepts
  • Linear Regression (both Univariate and Multivariate)
  • Polynomial Regression (both Univariate and Multivariate)
  • Logistic Regression (Binary Class)
  • Logistic Regression (Multi-Class)
  • K-NN Classification
  • Naive Bayesian classifiers
  • SVM - Support Vector Machines
  • Experiential Learning project
Unsupervised Learning
  • Unsupervised Learning concepts
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • High-dimensional clustering
  • Expectation Maximization

Ensemble technique

  • Decision Trees
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Stacking
  • Experiential Learning project
  • PCA (Principal Component Analysis) and Its Applications
  • Confusion Matrix

Reinforcement Learning

  • Value-based methods Q-learning
  • Policy-based methods

Recommendation Systems

  • User & item-based recommendation systems
  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation systems
  • Performance measurement
  • Experiential Learning project

Featurization, model selection & tuning

  • Text Analytics
  • Feature extraction
  • Model Defects & Evaluation Metrics
  • Model selection and tuning
  • Comparison of Machine Learning models
  • Experiential Learning project

Tools and Techniques

  • Python (Pandas, Numpy, Scipy,
  • Matplotlib, Seaborn, and Scikit-Learn)
  • Mini Projects
  • Machine Learning Lab session

Artificial Intelligence

Deep Learning
  • Neural Network Basics
  • Artificial Neural Network (ANN)
  • Forward Propagation
  • Backward Propagation
  • Deep Neural Networks
  • Recurrent Neural Networks (RNN)
  • Deep Learning applied to images using CNN
  • Tensor Flow for Neural Networks & Deep Learning

Computer Vision

  • Convolutional Neural Networks
  • Keras library for deep learning in Python
  • Pre-processing image Data
  • Object & face recognition

Visualization

  • Visualizing features & kernels
  • TensorBoard – Visualizing Learning, Graph Visualization
  • Synthesis and style transfer
  • Case Study: Visualizing a convoluted neural network

Natural Language Processing

  • NLP library NLTK
  • Statistical NLP and text similarity
  • Syntax and parsing techniques
  • Text summarization techniques
  • Semantics and Generation

Intelligent Agents

  • Uninformed and heuristic-based search techniques
  • Adversarial search and its uses
  • Planning and constraint satisfaction techniques

Language & tools

  • Python
  • Data libraries like Pandas, Numpy, Scipy
  • Python ML library sci-kit-learn
  • Python visualization library Matplotlib
  • NLP library NLTK
  • Tensor Flow
  • Keras

Capstone project

  • Group Presentation

Admission details

To get admission to the Masters in Artificial Intelligence and Machine Learning online training, follow the steps mentioned below:

Step 1. Follow the link below to open the official Careerera website.

(https://www.careerera.com/artificial-intelligence-and-machine-learning/masters-in-artificial-intelligence-and-machine-learning)

Step 2. By selecting the 'Upcoming Batches' button, candidates can choose their batch.

Step 3. Click the 'Enroll Now' button to begin the application process.

Step 4. Fill out the necessary information and submit the necessary documents.

Step 5. Pay the program fee and start training on the designated day.

How it helps

Candidates pursuing Masters in Artificial Intelligence and Machine Learning course will be benefited in the following ways:

  • Enhance future growth prospects with this Master of AI and Machine Learning from Careerera.
  • Learn how artificial intelligence (AI) engages intelligent systems with programs to learn.
  • Investigate the usage of enhanced perceptual Abilities in machine intelligence.

FAQs

Is there any exam for Masters in Artificial Intelligence and Machine Learning certification?

Yes, Learners will have to pass an exam for Masters in Artificial Intelligence and Machine Learning certification from Careerera.

Who is the course provider of the Masters in Artificial Intelligence and Machine Learning course?

Careerera is the course provider of the Masters in Artificial Intelligence and Machine Learning course.

Can I change my Masters in Artificial Intelligence and Machine Learning online course batch?

Yes, candidates can change their Masters in Artificial Intelligence and Machine Learning online course batch according to their availability.

How long does it take to complete the Masters in Artificial Intelligence and Machine Learning course?

It takes 12 months to complete the Masters in Artificial Intelligence and Machine Learning course.

What are the career scopes after completing the Masters in Artificial Intelligence and Machine Learning training?

There are various career scopes such as Data scientist, IT professional, Data professional after the completion of the Masters in Artificial Intelligence and Machine Learning training.

Trending Courses

Popular Courses

Popular Platforms

Learn more about the Courses

Download the Careers360 App on your Android phone

Regular exam updates, QnA, Predictors, College Applications & E-books now on your Mobile

Careers360 App
150M+ Students
30,000+ Colleges
500+ Exams
1500+ E-books