- Statistical analysis concepts
- Descriptive statistics
- Introduction to probability and Bayes theorem
Online
12 Months
Quick facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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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
certificate providing authority
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
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.
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.
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.
The syllabus
Statistical Learning
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
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
Yes, Learners will have to pass an exam for Masters in Artificial Intelligence and Machine Learning certification from Careerera.
Careerera is the course provider of the Masters in Artificial Intelligence and Machine Learning course.
Yes, candidates can change their Masters in Artificial Intelligence and Machine Learning online course batch according to their availability.
It takes 12 months to complete the Masters in Artificial Intelligence and Machine Learning course.
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.