• +91 9346698103
  • Madhapur, Hyderabad.

Machine Learning(ML) with AI

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Unlocking the Power of Intelligent Systems


This course provides a deep dive into Machine Learning (ML) and Artificial Intelligence (AI), covering fundamental concepts, algorithms, and real-world applications. It explores supervised and unsupervised learning, deep learning, neural networks, and AI-driven decision-making using industry-standard tools like Python, TensorFlow, and Scikit-Learn.


Students will gain hands-on experience in building predictive models, AI-driven automation, and natural language processing (NLP). By the end of the course, learners will be equipped with the skills to develop intelligent systems for various industries, including healthcare, finance, and robotics.

Course Overview

This course covers the core principles of Machine Learning and Artificial Intelligence, focusing on algorithms, neural networks, and AI-driven applications. Using tools like TensorFlow, Scikit-Learn, and Python, students will learn to develop and deploy intelligent systems for automation and decision-making.

What You'll Learn in This Course:

  • Understand the key concepts, types of machine learning, and AI applications.
  • Train models for classification, regression, and clustering.
  • Build AI models with TensorFlow, Keras, and PyTorch.
  • Develop AI solutions for text and speech recognition.
  • Implement AI models for image recognition and decision-making tasks.
  • Apply ML and AI to healthcare, finance, robotics, and automation.

This course will equip you with the knowledge and hands-on experience to develop intelligent AI systems, automate decision-making, and build advanced machine learning models for real-world applications.

Course Content

  • 1.1 Introduction to ML & AI
  • 1.2 Data Preprocessing & Feature Engineering
  • 1.3 Regression Models
  • 1.4 Classification Models
  • 2.1 Ensemble Learning
  • 2.2 Support Vector Machines (SVM) & Hyperparameter Tuning
  • 2.3 Clustering & Dimensionality Reduction
  • 2.4 Model Evaluation & Metrics
  • 3.1 Introduction to Deep Learning
  • 3.2 Backpropagation & Neural Network Implementation
  • 3.3 Convolutional Neural Networks (CNN)
  • 3.4 CNN Architectures & Image Classification
  • 4.1 NLP Basics
  • 4.2 Word Embeddings & Sentiment Analysis
  • 4.3 RNNs & Text Generation
  • 4.4 NLP Mini-Project
  • 5.1 Introduction to Reinforcement Learning
  • 5.2 Advanced RL Techniques
  • 5.3 Model Deployment
  • 5.4 Deployment Mini-Project
  • 6.1 End-to-End Data Science Project Setup
  • 6.2 Model Selection & Fine-Tuning
  • 6.3 Resume Building & Portfolio Projects
  • 6.4 Final Capstone Project
  • 7.1 Python, Machine Learning, and Deep Learning Basics
  • 7.2 Introduction to Generative Models
  • 7.3 Large Language Models (LLMs)
  • 7.4 LLM Fine-Tuning
  • 8.1 Retrieval-Augmented Generation (RAG)
  • 8.2 Agents and Autonomous Systems
  • 8.3 Optimization & Deployment
  • 8.4 Capstone Project