AI | ML

AI/ML, which stands for artificial intelligence (AI) and machine learning (ML), is a significant evolution in computer science and data processing that is rapidly transforming a wide range of industries.

As businesses and other organizations transform digitally, they face an increasing flood of data that is both extremely valuable and increasingly difficult to collect, process, and analyze. New tools and methodologies are required to manage the massive amounts of data being collected, mine it for insights, and act on those insights once they are found.

We Follow the official Curriculum provided  and we follow a standard practically approach to trains our student to easily get jobs in various IT Industries.

Advantages of AI/ML:

• The speed and effectiveness of data analysis is one of the main advantages of AI and ML. Conventional research methods necessitate substantial resources and can be laborious in terms of data collection and analysis. Contrarily, AI and ML can process massive amounts of data rapidly and deliver insights in real-time, enabling organizations to react to shifting market conditions faster.
• The potential to lower expenses and improve accuracy is provided by AI and ML. Businesses may make more confident judgments based on data when they train algorithms to see patterns and make highly accurate forecasts. Businesses can also cut costs related to data collecting and analysis by automating some parts of the research process.
• Additionally, personalization is made possible by AI and ML, which enables companies to focus their research on certain goals. For example, AI and ML can assist in locating niche markets or customer segments that are challenging to study through conventional techniques, offering insights that can guide product development or marketing plans.

Syllabus:

• Python fundamentals
• Overview and introduction
• Install and Configure Variable Types and Input Basic Operators in the Python PyCharm IDE
• Loops for making decisions, date and time numbers, and string functions
• Integrated Modules
• Slice and Slicing with Python
• OOPS Concept
• Python’s Classes and Self-Class Constructor Inheritance, Types, and Polymorphism API
• Foundational Rest Architecture
• Python and rest
• Python libraries
• Learn Feature Engineering with Numpy, Matplotlib, and Pandas
• Choosing Features
• Data Enrichment
• Information Visualization
• Information Visualization Practice data analysis using Apple Dimensionally Reduction datasets.
• PCA, LDA Kernel Method
• Supervised instruction
• Techniques for Regression and Classification:
• Regression and Classification
• Decision Tree Method using Support Vector Machine and Linear Regression
• Deep Learning using Naive Bayes and Logistic Regression
• Overview of Neural Networks
• Neural Network Types
• Convolution and Reverse Propagation in Neural Network-Based Image Processing Tensorflow and Keras
• Neural Network Transfer Learning Image Augmentation 

Prerequisite:

• Strong mathematical knowledge
• Good understanding of programming languages
• Strong understanding of data analytics and modeling.