Course Overview

Module 1: Python Fundamentals

Covering Python basics, control structures, object-oriented programming, data structures, and file input/output operations. Learn about variables, data types, operators, loops, conditionals, functions, classes, objects, inheritance, lists, tuples, dictionaries, file handling, and error handling. Gain essential skills in Python programming for building robust applications and handling data effectively.)

Module 2: SQL Fundamentals

 Explore relational databases, SQL basics, querying multiple tables with joins, aggregating data with GROUP BY and HAVING clauses, and utilizing subqueries. Learn to create tables, insert and manipulate data, perform various types of joins, aggregate data using GROUP BY, and employ subqueries for advanced querying. Gain essential skills in SQL for effective database management and analysis.

Module 3: Data Exploration and Visualization with Tableau and Power BI

 Discover Tableau and Power BI for data exploration and visualization. Learn to connect to data sources, create visualizations, and build interactive dashboards. Dive into creating charts, graphs, maps, calculated fields, and table calculations. Explore interactivity features, including filters, parameters, and actions. Master the art of storytelling with data using storyboards in Tableau and Power BI. Enhance your skills in data visualization and communication.

Module 4: Machine Learning Fundamentals

 Explore the foundations of Machine Learning. Learn about supervised and unsupervised learning, linear regression, and model evaluation metrics. Discover clustering algorithms such as K-means and hierarchical clustering. Explore dimensionality reduction techniques like PCA and t-SNE. Master model selection and tuning using grid search and cross-validation. Gain essential skills in Machine Learning for data analysis and predictive modeling.

Module 5: Deep Learning Fundamentals

 Delve into the world of Deep Learning. Start with an introduction to neural networks, covering feedforward networks, backpropagation, and activation functions. Explore Convolutional Neural Networks (CNN) for tasks like image classification and object detection. Dive into Recurrent Neural Networks (RNN) for sequence modeling and sentiment analysis. Discover autoencoders and Generative Adversarial Networks (GAN) for tasks like image generation and image-to-image translation. Master the fundamentals of Deep Learning for advanced data analysis and artificial intelligence applications.

Module 6: Artificial Intelligence Fundamentals

 Dive into the exciting field of Artificial Intelligence. Begin with an introduction to AI, covering agents, environments, and search algorithms. Explore logic and planning with propositional and first-order logic, as well as planning techniques. Discover Natural Language Processing (NLP) for tasks like text classification, information extraction, and sentiment analysis. Delve into robotics, covering kinematics, dynamics, path planning, and control. Gain foundational knowledge in AI for tackling real-world challenges.