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- Jupyter notebook – Installation & function
- Python functions, packages and routines
- Pandas, NumPy, Matplotlib, Seaborn
- Working with data structures,arrays, vectors & data frames
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- Post Graduate Program in Artificial Intelligence and Machine Learning
Post Graduate Program in Artificial Intelligence and Machine Learning
Online
12 Months
₹ 390,000
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
certificate availability
certificate providing authority
The syllabus
Foundations
Python for AI & ML
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
Academic Director
Great Learning
Other Bachelors, Other Masters, Ph.D
Mr Mukesh Rao
Professor
Great Learning
Mr Gurumoorthy Pattabiraman
Faculty
Great Learning
Other Masters
Dr D Narayana
Professor
Great Learning
Ph.D
Mr Sayan Dey
Independent Consultant
Freelancer
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