TopTech MNC Employees Instructors:

Learn directly from industry leaders! Our courses feature instructors who are

Top tech MNC employees, bringing real-world experience and practical insights into the classroom. Stay ahead of the curve with knowledge that goes beyond traditional textbooks.

Partnership withTelangana's Largest IT Startup Incubators: As trail blazers in the education space, we've established partnerships with Telangana's largest IT startup incubators, including the renowned WowWarangal Startup Incubators. Immerse yourself in an environment that nurtures innovation and prepares you for the dynamic world of startups.

Top MNC Industry Connections:

Connect with the best in the industry through our extensive network of top multinational companies. Benefit from exclusive interactions, workshops, and networking opportunities with professionals at the forefront of technological advancement

Industry Connections:

Our commitment t o bridging the gap between academia and industry is evident in our strong industry connections. Engage with industry experts, attend conferences, and join forums that provide valuable insights into the latest trends a n d developments.

Internship Opportunities:

Gain hands-on experience through exclusive internship opportunities with leading

companies. Apply your knowledge in practical settings, develop a robust skill set, and enhance your employability in the competitive tech landscape.

PlacementAssistance:

Your success is our priority! Our dedicated placement assistance program is designed to guide you toward rewarding career opportunities. Leverage our connections with top companies for a smooth transition from student to

professional.

Global Standard Curriculum

Week 1-2: Introduction to Machine

Learning and Al

Overview of Machine Learning (ML) and Al

Types of Machine Learning: Supervised,

Unsupervised, and Reinforcement

Learning

Applications of Al in Various Industries Ethical Considerations in A

Week 3-4: Python Programming for

Machine Learning

Introduction to Python for Data Science

NumPy and Pandas for Data Manipulation

Matplotlib and Seaborn for Data

Visualization

Jupyter Notebooks for Interactive

Computing

Week 5-6: Exploratory Data Analysis (EDA) Data Cleaning and Preprocessing Statistical Analysis and Visualization Feature Engineering

Handling Missing Data

Week 7-8: Supervised Learning Algorithms

Linear Regression and Logistic Regression Decision Trees and Random Forests

Support Vector Machines (SVM) K-Nearest Neighbors (KNN)

SCIENC ENGLISH

MATH

Week 9-10: Unsupervised Learning Algorithms

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA) Association Rule Mining

 Week 11-12: Neural Networks and

Deep Learning

Introduction to Neural Networks Feedtorward Neural Networks Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN)

Week 13-14: Natural Language Processing (NLP)

Introduction to NLP

Text Processing and Tokenization Sentiment Analysis

Named Entity Recognition (NER)

Week 15-16: Reinforcement Learning

Introduction to Reinforcement Learning Markov Decision Processes (MDP)

Q-Learning

Deep Q Network (DQN)

Week 17-18: Al and Ethics

Bias and Fairness in Machine Learning

Explainability and Interpretability Privacy Concerns ni Al

Al in Healthcare and Responsible Al

Week 19-20: Machine Learning in Production

Model Deployment and Serving Building RESTful APis for ML Models Containerization with Docker

Introduction to Model Monitoring

Week 21-22: Al Applications Computer Vision Applications Speech Recognition and Synthesis

Recommendation Systems Al in Finance and

Industry-specific Applications

Week 23-24: Capstone Project

Participants work on a machine

learning project

Integration of skills learned

throughout the course Presentation and Documentation

Week 25: Review and Exam Preparation

Comprehensive review of key concepts

Practice exams and quizzes

Exam preparation tips and resources

Week 26: Final Exam