Master in Machine Learning & AI Course

NSIM Institute has proudly marked a decade of excellence in the realm of Machine Learning and Artificial Intelligence training. Over the past Ten years, NSIM has consistently demonstrated its commitment to providing top-notch education in these cutting-edge technologies. With a focus on quality education, the institution has empowered countless students with the knowledge and skills needed to thrive in the ever-evolving field of AI and ML. NSIM’s decade-long journey is a testament to its dedication to nurturing talent and shaping the future of AI and ML professionals. (NSIM) stands out as a beacon of excellence in education and job placement for Machine Learning with AI. With a cutting-edge curriculum and dedicated faculty, NSIM empowers its students with a deep understanding of machine learning and artificial intelligence. The institution’s commitment to practical learning and real-world projects equips graduates with the skills demanded by industry leaders. NSIM’s strong industry connections and placement support ensure that students have a smooth transition from the classroom to their dream careers, making it the ideal choice for those seeking the best education and job placement opportunities in the field of Machine Learning with AI.

With AI and machine learning becoming increasingly vital across industries your future prospects are brighter, than before. Join NSIM today. Step into a world filled with possibilities, where your dreams can become your reality.

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    Course Fee

    EMI starts at 14,554/-


    6 Months


    Offline / Online

    Placement Assistance

    100 % Practical Training

    Live Projects

    Case Study

    Course Curriculum

    • Deep Learning vs Machine Learning
    • History of ML
    • AI, ML, Deep Learning, Data Science
    • Examples of applied ML
    • Applications of ML
    • Fairness-related harms
    • Unfairness assessment and mitigation
    • Over or Under Representation
    • Detecting Unfairness
    • Understand your models and build in fairness
    • Define fairness metrics
    • Mitigating unfairness
    • K-nearest neighbours
    • logistic regression
    • Support vector machine
    • Ensemble method
    • Random forests
    • Boosting
    • Ridge regression
    • Tikhonov regression
    • polynomial regression
    • Support vector regression
    • Kernel PCA
    • Local linear embedding
    • Multidimensional scaling
    • Label spreading
    • Label propagation
    • Sentiment Analysis
    • Translation and sentiment analysis with ML
    • Some Definitions
    • Time Series Data Characteristics
    • ARIMA model
    • Check model accuracy

    This course will equip you with valuable insights and practical tips to excel in digital marketing interviews, helping you stand out as a knowledgeable and well-prepared candidate.

    AI Specializations

    • Image segmentation
    • Object detection
    • Image generation
    • Named Entity Recognition (NER)
    • Text summarization
    • Dialog systems
    • Introduction to GANs
    • Training GANs
    • Applications of GANs
    • Medical image analysis
    • Disease prediction
    • Ethical considerations

    Who Can Attend

    Students, working professionals and others

    from various backgrounds.

    Get Course Brochure

    Master in Machine Learning & AI Course EMI starting from ₹14,55

    Programme Outcome

    • Writing scripts to efficiently read and manipulate CSV, XML, and JSON files
    • Quickly and efficiently parsing executables, log files, pcap and extracting * artifacts from them
    • Making API calls to merge datasets
    • Use the Pandas library to quickly manipulate tabular data
    • Effectively visualizing data using Python
    • Pre-processing raw security data for machine learning and feature engineering
    • Building, applying and evaluating machine learning algorithms to identify potential threats
    • Automating the process of tuning and optimizing machine learning models
    • Hunting anomalous indicators of compromise and reducing false positives
    • Use supervised learning algorithms such as Random Forests, Naive Bayes, K-Nearest Neighbours (K-NN) and Support Vector Machines (SVM) to classify malicious URLs and identify SQL Injection
    • Apply unsupervised learning algorithms such as K-Means Clustering to detect anomalous behaviour

    Who Should Join the Course

    • Information Security Professionals who want to apply machine learning algorithms in security.
    • Graduate Freshers to build up the profile and kick-off the career.
    • Computer Science, Electronics and Communication, Electricals Domain working professionals.

    Career Opportunities in Machine

    ML/MLOps Engineer



    Data Scientist

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