Post Graduate Programme in Data Science, AI-ML & Deep Learning 

offered by JG University

Introduction to Data Science, Artificial Intelligence, Machine Learning and Deep Learning fundamentals and understanding of their benefits to the industries. Discussion on case studies for deeper understanding

Developing the skills of R & Python programming, along with Hands-on experience. This knowledge will be useful for developing logic in Data science, AI-ML and Deep learning applications

Various statistical models such as T-Test, Anova, Bayes theorem, Standard Distribution, Chi-Square Test to analyse the data which are useful for getting the business insights by using industry data to overcome the problems

Apply the acquired learning with Hands-on session

Understand the data better to prescribe and predict the future by applying advance statistical methods such as descriptive statistics, Correlation analysis, Z Test, IQR Kurtosis and Skewness which are useful for making business-critical decisions

Apply the acquired learning with Hands-on session

Working with NumPy libraries to perform a wide variety of mathematical and scientific calculations. This creates a foundation for building Machine Learning algorithms

Apply the acquired learning with Hands-on session

Understand the working of Pandas, an open-source Python package, which is used for data science/data analysis and machine learning projects

Apply the acquired learning with Hands-on session

Knowledge of data visualisation techniques and the use of Power BI / Tableau. Participants can develop an analytical dashboard for management in an organisation

Apply the acquired learning with Hands-on session

Understanding of technologies such as Predictive Equation, Gradient Descent Algorithm, OLS Approach, R2, MAPE, and RMSE to predict the value of variables

This knowledge is useful for the prediction of relationship between variables and forecasting in Machine Learning

Apply the acquired learning with Hands-on session

Understanding of mathematical modelling, Sigmoid function, Confusion Matrix Analysis, SKLearn, F1 Score, etc. to understand the relationship between the dependent and independent variables by estimating probabilities using a logistic regression equation

This type of analysis can help you predict the likelihood of an event happening or a choice being made

Apply the acquired learning with Hands-on session

Understanding of KNN techniques such as distance matrix, regression and classification, over-fitting and under-fitting, etc. for Supervised Learning

Knowledge of mathematics forming Decision Tree, Entropy & Gini Entropy Approach, Variance, visualization using graph-viz with Hands-on

Both methods are used to develop Machine Learning models

Understanding of concepts and working principles, mathematical modelling, Slack Variable, Kernel method and Non-linear Hyperplanes to solve both classification and regression problems using SVM

Understanding of concepts, bagging and boosting, Random Forest, Gradient Boosting Trees, XGBoost, AdaBoost, etc. to solve both classification and regression problems using Ensemble Learning

Apply the acquired learning with Hands-on session

Understanding of K Means Clustering, Hierarchical Clustering, Customer Segmentation, Dimensionality Reduction, Data Compression, Multicollinearity, Factor analysis to develop an unsupervised learning model

The main goal of unsupervised learning is to discover hidden and interesting patterns in unlabeled data

Apply the acquired learning with Hands-on session

Understanding of Simple and Weighted Moving Average method, Single/Double/Triple Exponential Smoothing method, ARIMA models, etc. for Time series prediction

It involves building models through historical analysis and using them to make observations and derive future strategic decision-making

Apply the acquired learning with Hands-on session

Understanding of Curse of Dimensionality, Multicollinearity, Factor analysis.

Anomaly detection using Moving Average Filtering Mean, Standard Deviation, Statistical approach, Isolation Forest, One Class SVM, etc

Principal component analysis (PCA) simplifies the complexity of high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as summaries of features

Apply the acquired learning with Hands-on session

Understanding the concept of Image processing /acquisition/ manipulation/ scaling, Video processing, Edge detection, Corner detection, Face detection, Object detection using OpenCV

Apply the acquired learning with Hands-on session

Understanding of NLTK and Textblob, Tokenization, Stemming and Lemmatization, TF-IDF, Count Vector, Sentiment Analysis using Google, Bing, IBM Speech to Text API

Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks

Apply the acquired learning with Hands-on session

Understanding of popularity, content, collaborative based filtering to suggest the relevant items to users

Apply the acquired learning with Hands-on session

Understanding of Tensor Board, Linear & Logistic regression and data manipulation using Tensor flow

Learning to work with Theano and building Linear & Logistic regression with Theano

An open-source software library to carry out numerical computation using data flow graphs, the base language for TensorFlow is C++ or Python, whereas Theano is a completely Python based library that allows users to define, optimize and evaluate mathematical expressions evolving multi-dimensional arrays efficiently

Apply the acquired learning with Hands-on session

CNN is a Neural Network that has one or more convolutional layers and is used mainly for image processing, classification, segmentation and also for other autocorrelated data

Learning of CNN Architecture, Convolution process, Maths behind CNNs, MaxPooling, Efficient Convolution Algorithms, Neuroscientific basis for Convolutional Networks, implementing CNN using Keras and Digit classification

Apply the acquired learning with Hands-on session

Recurrent neural networks (RNN) are a class of Neural Networks that are helpful in modelling sequence data

Understanding the basic concept of RNN, Vanishing and Exploding gradient problems, LSTM Networks, LSTM for NLP, Word Embedding, Text Classification, Stochastic Encoders and Decoders, etc

RNNs exhibit similar behaviour to how human brains function and produce predictive results in sequential data that other algorithms can't

Apply the acquired learning with Hands-on session

Understanding of Single Layer Perceptron Model, Multilayer and Feed Forward Neural Network, Cost Function Formation, Backpropagation Algorithm

Artificial Neural Networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data

Apply the acquired learning with Hands-on session

Understanding of activation functions for Neural Networks, Optimization techniques- SGD, ADAM, LBFGS, Momentum in Neural Networks, Softmax classifier and ReLU classifier, Deep Neural Networks

Neural networks reflect the behaviour of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of Artificial Intelligence, Machine Learning and Deep Learning

Apply the acquired learning with Hands-on session