Artificial Intelligence Course in Chennai

Excel Core Concepts

• Basics: Data input, formula referencing, name range, logical functions, conditional formatting, and advanced validation.
• Advanced Features: Dynamic tables, sorting, filtering, pivot tables, dashboards, data and file security, and VBA macros.
• Excel VBA: Exploring ranges, worksheets, loops, IF conditions, and debugging.

Data Analysis using Excel

• Data Handling: Text data processing, date management, data conversion, handling of missing values, and table operations.

Visualising Data:

• Using charts, pie charts, scatter plots, bar graphs, column charts, line graphs, maps, and more.

Excel Power Tools:

• Introduction to Power Pivot, Power Query, and Power View.

Classification in Excel:

• Topics include binary and multiple classification problems, confusion matrices, AUC, and ROC curves.

Understanding Information Measures:

• Probability, entropy, mutual information, and dependence.

Regression Analysis in Excel:

• Covering topics like standardisation, probability distributions, inferential statistics, linear and logistic regression, error measures, and more.

Practical Sessions:

• Practical exercises focusing on real-world problem scenarios.

Basic SQL Concepts:

• Introduction to SQL: Understanding tables, joins, and variables.

Advanced SQL Techniques:

• Exploring SQL functions, subqueries, views, string and mathematical functions, date-time functions, and more.
• User-Defined Functions: Discussing types, stored procedures, rank functions, triggers, etc.
• SQL Performance: Delving into record grouping, searching, sorting, indexing, and more.
• Practical Sessions: Engaging with practical exercises on real-world problem scenarios.

Data Integration:

• Extract, Transform, Load (ETL) Methods: Web scraping and API interactions.

Data Management with Python Libraries:

• Using NumPy: Exploring arrays, linear algebra concepts, CRUD operations, and more.

Manipulating Data with Pandas:

• Loading data, understanding data frames and series, CRUD operations, and data segmentation.

Data Preparation:

• Preliminary Data Inspection: EDA, feature engineering and scaling, normalisation, outlier analysis, VIF, and more.

Visualising Data with Python:

• Using Matplotlib: Creating bar charts, scatter plots, line graphs, pie charts, etc.
• Using Seaborn: Producing regression plots, categorical graphs, area charts, and more.
• Practical Sessions

Descriptive Statistics

• Measures of central tendency
• Measures of spread
• Five points summary

Probability Theory

• Probability Distributions
• Central limit theorem
• Bayes theorem

Inferential Statistics

• Correlation and covariance
• Confidence intervals
• Hypothesis testing: F-test, Z-test, t-test, ANOVA, chi-square test, and more

Machine Learning Overview

• Supervised vs. Unsupervised learning
• Introduction to tools: sci-kit-learn, Keras, and more

Regression Analysis

• Understanding regression problems: Dependent and independent variables
• Training, evaluating, and enhancing the performance of regression models

Classification Analysis

• Understanding classification problems: Dependent and independent variables
• Training, evaluating, and enhancing the performance of classification models

Clustering Analysis

• Understanding clustering problems: Dependent and independent variables
• Training, evaluating, and enhancing the performance of clustering models

Methods in Supervised Learning

• Linear Regression: Building models for linear data with techniques like data preprocessing and normalization
• Logistic Regression: Model building for binary outcomes
• Decision Trees and Random Forests: Hierarchical model building
• Support Vector Machines: For both regression and classification tasks
• Gradient Descent: Iterative method to find function extremities
• K-Nearest Neighbors: Classification method based on proximity
• Time Series Analysis: Predictive analysis using time series data

Techniques in Unsupervised Learning

• K-means Clustering: Grouping data based on similarities
• Dimensionality Reduction: Managing multi-dimensional data
• Linear Discriminant Analysis and Principal Component Analysis: Optimising multi-dimensional data

Performance Evaluation Metrics

• Classification reports: Metrics such as recall, precision, and more
• Confusion matrix: Assessing prediction outcomes
• Metrics like r2, adjusted r2, and mean squared error

Foundations of AI

• Introduction to TensorFlow and Keras API

Understanding Neural Networks

• Basic Neural Networks
• Multi-layer Neural Networks
• Advanced Artificial Neural Networks

Techniques in Deep Learning

• Deep Neural Architectures
• Convolutional Neural Networks
• Recurrent Neural Networks
• GPU applications in deep learning
• Exploration of Autoencoders and restricted Boltzmann machines

Introduction to Power BI

• Overview of Power BI, its components, and BI Tools
• Data Warehousing essentials
• Workflow with Power BI Desktop and Data Extraction
• Data handling with SaaS Connectors, Azure SQL database, and integrating Python and R
• Advanced features: Power Query Editor, Data Transformations, and Hierarchies

DAX Fundamentals

• Data Modeling concepts
• Time-based functions in DAX and advanced functionalities

Advanced Data Visualization

• Techniques such as Slicers, filters, and Drill Down Reports
• Exploring Power BI Query, Q & A features, and Data Insights
• Power BI settings, administration, and direct connections
• Embedded Power BI API and mobile integration

Practical Session:

• Hands-on experience creating a visual dashboard based on sales data