Master in Machine Learning
& AI Systems
NSIM Institute proudly celebrates a decade of excellence in Machine Learning and Artificial Intelligence training. Over the past ten years, NSIM has delivered high-quality education, empowering students with the expertise needed to excel in AI and ML. Known for its cutting-edge curriculum and dedicated faculty, NSIM provides practical learning experiences and strong industry connections to ensure successful career transitions. As AI and machine learning grow in importance, NSIM stands out as a top choice for those seeking outstanding education and job placement in these fields. Join NSIM and unlock a world of opportunities to turn your aspirations into reality.
Job Placement Connections
Skills Assessment & Estimation Training
Resume and LinkedIn Profile Development
Mastering Negotiation and Persuasion Skills
Practice Job Interviews and Placement
Digital Marketing Career Opportunities
Guaranteed Placement Assistance for All Students
9 LPA
Highest Package
4.5 LPA
Average Package
90%
Placement Rate
50%
Average Salary Hike
*Placement Statistics According to the 2024 Internal Report.
Our Alumni Partner with Leading Brands Such As
Our graduates can collaborate with prestigious brands across various industries, leveraging their skills in digital marketing strategies, SEO optimization, social media management, and more. Their expertise enhances the brands’ online presence and showcases the high level of training and knowledge they’ve acquired through our program. This hands-on experience with leading companies highlights their ability to drive impactful results and adapt to the ever-evolving digital landscape.
Connect with CXOs and CMOs from Top Digital Brands
Curriculum Developed in Collaboration with Industry Experts
Course Modules
Module 1: Introduction to Machine Learning
Start with the basics of machine learning, understanding its principles, methodologies, and applications. This module covers different types of learning methods, such as supervised, unsupervised, and reinforcement learning, laying the groundwork for more advanced topics.
Module 2: ML and Fairness
Explore the ethical considerations in machine learning, focusing on fairness and bias. Learn how to evaluate and mitigate biases in machine learning models to ensure that your solutions are equitable and just.
Module 3: Representing Information and Preparing Data for Use with ML Methods
Learn how to effectively represent and prepare data for machine learning tasks. This module covers data preprocessing techniques, feature extraction, and data transformation to ensure your models perform optimally.
Module 4: Classifiers and Feature Importance
Study various classification algorithms and their applications. Understand how to assess the importance of different features in your models and how these features impact model performance.
Module 5: Decision Trees
Dive into decision tree algorithms, a fundamental technique in machine learning. Learn how decision trees work, how to build them, and how to interpret their results. Explore their strengths and limitations in different scenarios.
Module 6: Regression and Combating Overfitting
Understand regression techniques used for predicting continuous outcomes. This module also addresses the issue of overfitting and strategies to prevent it, ensuring your models generalize well to new data.
Module 7: Nonlinear Dimensionality Reduction
Learn about techniques for reducing the dimensionality of data while preserving its structure. This module focuses on methods to handle complex, high-dimensional data and improve model performance.
Module 8: Semi-Supervised Learning
Explore semi-supervised learning, where models are trained on a mix of labeled and unlabeled data. Learn how to leverage limited labeled data to improve model accuracy and performance.
Module 9: Unsupervised Learning: Mixture Models
Study unsupervised learning techniques, particularly mixture models. Learn how to identify patterns and group data into clusters without predefined labels, enhancing your ability to understand and organize data.
Module 10: Clustering ML Techniques
Delve into various clustering methods used to group similar data points. This module covers popular algorithms and techniques for finding natural groupings in data, with practical applications in data analysis.
Module 11: Natural Language Processing ML Techniques
Explore techniques for processing and analyzing textual data. This module covers natural language processing methods used to handle tasks like sentiment analysis, text classification, and language generation.
Module 12: Time Series Forecasting ML Techniques
Learn methods for analyzing and predicting time-dependent data. This module covers techniques for forecasting trends, seasonality, and anomalies in time series data.
Module 13: Introduction to Reinforcement Learning
Discover the basics of reinforcement learning, where agents learn to make decisions by interacting with their environment. Understand key concepts such as rewards, policies, and value functions.
Module 14: Real-World Applications for ML
Apply your knowledge to real-world scenarios. This module focuses on practical applications of machine learning across various industries, demonstrating how to implement and adapt techniques to solve real problems.
AI Specializations
Case 1: Computer Vision
Explore the field of computer vision, which focuses on enabling machines to interpret and understand visual information from the world. This specialization covers techniques for image and video analysis, object detection, and pattern recognition. Gain hands-on experience in developing systems that can analyze and interpret visual data to solve real-world problems.
Case 2: Natural Language Processing (Advanced)
Delve into advanced natural language processing (NLP) techniques to enhance the way machines understand and interact with human language. This specialization includes deep learning models for text analysis, sentiment analysis, and language generation. Learn to build and deploy sophisticated language models that can handle complex linguistic tasks.
Case 3: Generative Adversarial Networks (GANs)
Study Generative Adversarial Networks (GANs), a powerful technique for generating new data samples that resemble a given dataset. This specialization covers the principles of GANs, including their architecture and training methods. Learn how to create models that can generate realistic images, text, and other data types through adversarial training processes.
Case 4: AI in Healthcare
Examine the application of machine learning techniques in the healthcare industry. This specialization focuses on using data-driven approaches to improve patient outcomes, streamline medical processes, and advance diagnostic tools. Explore real-world case studies and projects that demonstrate how machine learning can be applied to solve critical challenges in healthcare.
