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ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

Artificial Intelligence & Machine Learning Training in Hyderabad (Ameerpet)

Build Intelligent Solutions with AI & ML — From Fundamentals to Real-Time Projects
At Openminds Technologies, we provide a powerful combo of Artificial Intelligence and Machine Learning training, designed to equip learners with in-demand skills for building smart, predictive, and self-learning applications. Learn from certified industry experts through a mix of theory, tools, and real-world project execution.

Course Highlights

✅ Learn Supervised & Unsupervised Learning, Deep Learning, NLP & Computer Vision
✅ Hands-on training with Python, TensorFlow, Keras, Scikit-learn, and Pandas
✅ Real-time projects across domains like finance, healthcare & automation
✅ Ideal for Data Scientists, AI Engineers & Tech Enthusiasts
✅ 100% Placement Assistance with Mock Interviews and Resume Support

What You Will Learn

  • AI Fundamentals & Real-World Use Cases

  • Python for AI/ML – Numpy, Pandas, Matplotlib

  • Regression, Classification, Clustering Algorithms

  • Neural Networks & Deep Learning with TensorFlow/Keras

  • Natural Language Processing (NLP)

  • Computer Vision and Image Processing

  • Deploying ML Models to Cloud/Apps

  • Real-Time AI/ML Project Development

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SYLLABUS

  • What is Artificial Intelligence?

  • Types of AI: Narrow, General, and Super AI

  • Introduction to Machine Learning

  • Difference between AI, ML, and Deep Learning

  • Real-world Applications of AI and ML

Download Syllabus

  • Basics of Python Programming

  • Python Libraries for AI/ML: NumPy, Pandas, Matplotlib, Scikit-learn

  • Data Types, Functions, Loops, and OOP Concepts

  • Working with DataFrames and Data Visualization

  • Linear Algebra: Vectors, Matrices

  • Probability and Statistics Basics

  • Mean, Median, Mode, Variance, and Standard Deviation

  • Introduction to Hypothesis Testing

  • Basics of Calculus and Gradient Descent

  • Introduction to Supervised Learning

  • Regression Algorithms: Linear Regression, Multiple Regression

  • Classification Algorithms: Logistic Regression, Decision Trees, Random Forest

  • Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1-Score

  • Introduction to Unsupervised Learning

  • Clustering Algorithms: K-Means, Hierarchical Clustering

  • Dimensionality Reduction: PCA (Principal Component Analysis)

  • Introduction to NLP Concepts

  • Text Preprocessing: Tokenization, Stemming, Lemmatization

  • Building Text Classification Models

  • Working with Sentiment Analysis

  • Introduction to Transformers (Basics)

  • Basics of Computer Vision

  • Image Classification with CNNs

  • Object Detection and Image Segmentation Concepts

  • Real-time Image Processing (Introduction Level)