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    Post Graduate Program in Artificial Intelligence and Machine Learning

    BY
    Great Learning

    Brochure Enquire
    Mode

    Online

    Duration

    12 Months

    Fees

    ₹ 375000

    Quick Facts

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

    Course and certificate fees

    Fees information
    ₹ 375,000

    The fees for course Post Graduate Program in Artificial Intelligence and Machine Learning is -

    HeadAmount
    Programme feesRs. 3,75,000 + GST

     

    Zero Cost EMI Plans -

    Admission Fees (payable on enrolment): ₹ 30,000 (including GST)

    Monthly Installment
    Installment No.Payment
    3 Month EMI₹ 1,43,400
    6 Month EMI₹ 71,700
    12 Month EMI₹ 35,850
    18 Month EMI₹ 23,900

    Note:

    1. Scholarship of 15k Available for selected candidates
    2. All amounts are including GST
    3. 2% processing charge for 12 month EMI and 3% processing for 18 month EMI

    EMI Plans -

    Admission Fees (payable on enrolment): ₹ 30,000 (including GST)

    Monthly Installment
    Installment No.Payment
    24 Month EMI₹ 20,251
    36 Month EMI₹ 14,289
    48 Month EMI₹ 11,541
    60 Month EMI₹ 9,899

    Note:

    1. Scholarship of 15k Available for selected candidates
    2. All amounts are including GST
    3. 3% processing charge
    certificate availability

    Yes

    certificate providing authority

    Great Learning

    The syllabus

    Foundations

    Python for AI & ML
    • Python Basics
    • Jupyter notebook – Installation & function
    • Python functions, packages and routines
    • Pandas, NumPy, Matplotlib, Seaborn
    • Working with data structures,arrays, vectors & data frames
    Applied Statistics
    • Descriptive Statistics
    • Inferential Statistics
    • Probability & Conditional Probability
    • Probability Distributions - Types of distribution – Binomial, Poisson & Normal distribution
    • Hypothesis Testing

    Machine Learning

    Supervised Learning
    • Multiple Variable Linear regression
    • Multiple regression
    • Logistic regression
    • K-NN classification
    • Naive Bayes classifiers
    • Support vector machines
    Unsupervised Learning
    • K-means clustering
    • Hierarchical clustering
    • High-dimensional clustering
    • Dimension Reduction-PCA
    Ensemble Techniques
    • Decision Trees
    • Random Forests
    • Bagging
    • Boosting
    Featurization, Model Selection & Tuning
    • Feature engineering
    • Model selection and tuning
    • Model performance measures
    • Regularising Linear models
    • ML pipeline
    • Bootstrap sampling
    • Grid search CV
    • Randomized search CV
    • K fold cross-validation
    Introduction to SQL
    • Introduction to DBMS
    • ER diagram
    • Schema design
    • Key constraints and basics of normalization
    • Joins
    • Subqueries involving joins and aggregations
    • Sorting
    • Independent subqueries
    • Correlated subqueries
    • Analytic functions
    • Set operations
    • Grouping and filtering

    Artificial Intelligence

    Introduction to Neural Networks and Deep Learning
    • Gradient Descent
    • Introduction to Perceptron & Neural Networks
    • Batch Normalization
    • Activation and Loss functions
    • Hyper parameter tuning
    • Deep Neural Networks
    • Tensor Flow & Keras for Neural Networks & Deep Learning
    Computer Vision
    • Introduction to Image data
    • Introduction to Convolutional Neural Networks
    • Famous CNN architectures
    • Transfer Learning
    • Object detection
    • Semantic segmentation
    • Instance Segmentation
    • Other variants of convolution
    • Metric Learning
    • Siamese Networks
    • Triplet Loss
    Natural Language Processing
    • Introduction to NLP
    • Preprocessing text data
    • Bag of Words Model
    • TF-IDF
    • N-grams
    • Word2Vec
    • GLOVE
    • POS Tagging & Named Entity Recognition
    • Introduction to Sequential models
    • Need for memory in neural networks
    • Types of sequential models – One to many, many to one, many to many
    • Recurrent Neural networks (RNNs)
    • Long Short Term Memory (LSTM)
    • GRU
    • Applications of LSTMs
    • Sentiment analysis using LSTM
    • Time series analysis
    • Neural Machine Translation
    • Advanced Language Models

    Capstone Project

    Career Assistance: Resume building and Mock interviews

    Instructors

    Dr Abhinanda Sarkar

    Dr Abhinanda Sarkar
    Academic Director
    Great Learning

    Other Bachelors, Other Masters, Ph.D

    Mr Mukesh Rao

    Mr Mukesh Rao
    Professor
    Great Learning

    Mr Gurumoorthy Pattabiraman

    Mr Gurumoorthy Pattabiraman
    Faculty
    Great Learning

    Other Masters

    Dr D Narayana

    Dr D Narayana
    Professor
    Great Learning

    Ph.D

    Mr Sayan Dey
    Independent Consultant
    Freelancer

    Dr Kumar Muthuraman

    Dr Kumar Muthuraman
    Professor
    Texas McCombs

    Ph.D

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