Artificial Intelligence (AI) Training in OMR

Python 

• Introduction to Python and IDEs The basics of the Python programming language and how you can use various IDEs for python development like Jupyter, Pycharm, etc.
• Python Basics: Variables, Data Types, Loops, Conditional Statements, Functions, Decorators, Lambda Functions, File Handling, Exception Handling, etc.
• Object-Oriented Programming: Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
• Hands-on Sessions And Assignments for Practice The culmination of all the above concepts is real-world problem statements for better understanding.

Linux

• Introduction to Linux: Establishing the fundamental knowledge of how Linux works and how you can begin with the Linux OS.
• Linux Basics: File Handling, data extraction, etc.
• Hands-on Sessions And Assignments for Practice Strategically curated problem statements for you to start with Linux.

SQL Basics: 

• Fundamentals of Structured Query Language
• SQL Tables, Joins, and Variables

Advanced SQL 

• SQL Functions, Subqueries, Rules, and Views
• Nested Queries, string functions, pattern matching
• Mathematical functions, Date-time functions, etc.

Deep Dive into User-Defined Functions

• Types of UDFs: inline table value, multi-statement table.
• Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimisation and Performance

• Record grouping, searching, sorting, etc.
• Clustered indexes, common table expressions.

Extract Transform and load

• Web Scraping and Interacting with APIs

Data Handling with NumPy

• NumPy Arrays, CRUD Operations, etc.
• Linear Algebra: matrix multiplication, CRUD operations, inverse, transpose, rank, determinant of a matrix, scalars, vectors, and matrices.

Data Manipulation Using Pandas

• Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

• Exploratory Data Analysis, Feature engineering, Feature scaling, Normalisation, standardisation, etc.
• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualisation

• Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
• Regression plots, categorical plots, area plots, etc. with Python Seaborn.

Descriptive Statistics:

• Measure of central tendency, measure of spread, five-point summary, etc.

Probability

• Probability distributions, Bayes’ theorem, and the central limit theorem.

Inferential Statistics:

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine Learning 

• Supervised, unsupervised learning.
• Introduction to scikit-learn, Keras, etc.

Regression 

• Introduction to classification problems, identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimise the efficiency of the regression model.

Classification 

• Introduction to classification problems, identification of a classification problem, and dependent and independent variables.
• How to train the model for a classification problem.
• How to evaluate the model for a classification problem.
• How to optimise the efficiency of the classification model.

Clustering 

• Introduction to clustering problems, identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimise the efficiency of the clustering model.

Supervised Learning 

• Linear Regression: Creating linear regression models for linear data using statistical tests, data preprocessing, standardisation, normalisation, etc.
• Logistic Regression: Creating logistic regression models for classification problems such as if a person is diabetic or not, if there will be rain or not, etc.
• Decision Tree: Creating decision tree models on classification problems in a tree-like format with optimal solutions.
• Random Forest: Creating random forest models for classification problems in a supervised learning approach.
• Support Vector Machine (SVM) or support vector machines for regression and classification problems.
• Gradient Descent: A gradient descent algorithm that is an iterative optimisation approach to finding the local minimum and maximum of a given function.
• K-Nearest Neighbours is a simple algorithm that can be used for classification problems.
• Time Series Forecasting: Making use of time series data and gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

• K-means is an algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction: handling multi-dimensional data and standardising the features for easier computation.
• Linear Discriminant Analysis: LDA, or linear discriminant analysis, is used to reduce or optimise the dimensions of multidimensional data.
• Principal Component Analysis (PCA) follows the same approach in handling multidimensional data.

• Classification reports: To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix: to evaluate the true positive/negative and false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics

• Introduction to keras API and TensorFlow

Neural Networks

• Neural networks
• Multi-layered Neural Networks
• Artificial Neural Networks

Deep Learning

• Introduction to Deep Learning (by Academic Faculty)
• Deep neural networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• GPU in deep learning
• Autoencoders, restricted Bolzmann machine

• Extracting, loading, and transforming data into usable format to gather insights.
• Data manipulation and handling to pre-process the data.
• Feature engineering and scaling the data for various problem statements.
• Model selection and model building on various classification and regression problems using supervised and unsupervised machine learning algorithms.
• Assessment and monitoring of the model created using the machine learning models.

• Recommendation Engine: The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
• Rating Predictions: This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
• Census: Using predictive modelling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
• Housing: This real estate case study will guide you towards real-world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
• Object Detection: A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real-time.
• Stock Market Analysis: Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
• Banking Problem: A classification problem that predicts consumer behaviour based on various features using machine learning models.
• AI Chatbot: Using the NLTK Python library, you will be able to apply machine learning algorithms and create an AI chatbot.