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Artificial Intelligence (AI) Course

Master AI in Hyderabad with 15+ projects. Learn Python, Machine Learning, Deep Learning, and NLP. Get certified and secure jobs in Hyderabad!

⏱ 3 Months🏫 Classroom📈 All Levels

Course Overview

Alright, so you're looking to get into Artificial Intelligence, huh? Good choice! This isn't just about buzzwords; it's about building systems that truly learn and adapt. At COSS, right here in Dilsukhnagar or Ameerpet, we're going to get you beyond the theory and straight into building real AI solutions. You'll kick things off with a solid Python foundation, moving quickly into Machine Learning algorithms. Think about it: Ever wondered how Amazon recommends products or how your bank flags fraud? You'll be building those kinds of predictive models yourself. We're talking 15+ hands-on projects, from predicting stock prices to classifying images, using tools like Scikit-learn and TensorFlow 2.x. After ML, we dive deep into Deep Learning – Convolutional Neural Networks for images, Recurrent Neural Networks for sequences, and even an intro to the Transformer architecture. You'll master frameworks like PyTorch and Keras. Then, we tackle Natural Language Processing, where you'll teach machines to understand human language, which is super critical for chatbots and sentiment analysis. Listen, the job market for AI specialists in Hyderabad is booming. Companies like Amazon Hyderabad and Microsoft IDC are constantly looking for talent in Gachibowli. Entry-level AI roles here often start around ₹6-9 LPA for those with strong practical skills. You'll also get prepared for certifications like the AWS Certified Machine Learning – Specialty (MLS-C01) exam, which really boosts your profile. Don't worry if you're working; we've got flexible weekend batches available.Dr.Ramesh has trained over 1,200 students, and you'll benefit from that real-world experience. Ready to build the future?

What You Will Learn

  • ✓ 15+ Hands-on AI & ML Projects
  • ✓ Master Python 3.10+ for AI
  • ✓ Deep Dive into TensorFlow 2.x & PyTorch
  • ✓ Learn NLP for Text & Speech Applications
  • ✓ AWS Certified ML – Specialty (MLS-C01) Prep
  • ✓ Expert Trainer Dr.Ramesh with real-world experience
  • ✓ Dedicated Placement Assistance (500+ success stories)
  • ✓ Weekend batches available for working professionals

Tools & Technologies

Python 3.10+NumPyPandasScikit-learnTensorFlow 2.xKerasPyTorchJupyter NotebookGoogle ColabNLTKspaCyOpenCVDockerGit

Syllabus

1Module 1: Python for AI & Data Science Fundamentals — Python 3.10+ Essentials & Libraries (NumPy, Pandas), Data Structures, Functions, and Object-Oriented Programming (OOP), Data Cleaning and Preprocessing Techniques, Data Visualization with Matplotlib & Seaborn, Introduction to Jupyter Notebooks & Google Colab+
2Module 2: Machine Learning Core Concepts — Supervised vs. Unsupervised Learning, Linear Regression & Logistic Regression, Decision Trees & Random Forests (Scikit-learn), Support Vector Machines (SVMs) & K-Nearest Neighbors (KNN), K-Means Clustering & Dimensionality Reduction (PCA), Model Evaluation Metrics & Hyperparameter Tuning+
3Module 3: Deep Learning with TensorFlow & Keras — Neural Network Architectures & Activation Functions, Building Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) for Image Recognition, Recurrent Neural Networks (RNNs) & LSTMs for Sequential Data, Transfer Learning and Fine-tuning Pre-trained Models, Introduction to TensorFlow 2.x and Keras+
4Module 4: Natural Language Processing (NLP) — Text Preprocessing: Tokenization, Stemming, Lemmatization, Word Embeddings: Word2Vec, GloVe, FastText, Sentiment Analysis & Text Classification, Introduction to Transformer Models (BERT, GPT overview), Using NLTK and spaCy Libraries, Building Chatbots and Q&A Systems+
5Module 5: Computer Vision & Reinforcement Learning Basics — Image Processing with OpenCV, Object Detection Concepts (YOLO, R-CNN overview), Image Segmentation & Facial Recognition basics, Introduction to Reinforcement Learning (Q-learning), Ethical Considerations & Bias in AI+
6Module 6: MLOps & AI Deployment Strategies — Model Versioning and Experiment Tracking (MLflow basics), Containerization with Docker for AI Applications, Introduction to Cloud AI Services (AWS SageMaker, Azure ML), Deploying AI Models as APIs (Flask/FastAPI), Monitoring and Maintaining AI Models in Production+
7Module 7: Capstone Project & Career Readiness — End-to-End Real-world AI Project Development, Project Presentation Skills & Documentation, Resume Building & Mock Interview Preparation, AWS Machine Learning – Specialty (MLS-C01) Exam Preparation, Discussion on Latest AI Trends and Career Paths+

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