Artificial Intelligence (AI & ML) Course in Hyderabad
Learn core AI and machine learning, from Python and statistics to deployed models
The Artificial Intelligence (AI & ML) course gives you a complete foundation in building intelligent systems. You start with Python, NumPy, pandas and the maths needed for machine learning: linear algebra, probability and statistics. You then study supervised learning, including regression, classification, decision trees, ensembles and gradient boosting, and unsupervised learning with clustering and dimensionality reduction, together with feature engineering, model evaluation, cross-validation and hyperparameter tuning using scikit-learn. The course introduces neural networks and deep learning with TensorFlow or PyTorch, then applies them to images and text, and gives an overview of generative AI and large language models, reinforcement learning ideas, responsible AI, and deployment of models as APIs with FastAPI and Docker. The course suits graduates and professionals who want a broad AI entry point; no prior programming is required, as Python is taught from the basics. You build several projects and a capstone. Classes run in classroom batches at the GloryTecks centre in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Introduction to AI & the ML Workflow
- •AI, machine learning and deep learning: how they relate
- •Supervised, unsupervised and reinforcement learning with examples
- •The ML project workflow from problem to deployment
- •Lab: Frame three business problems as ML problems
- •Outcome: Describe the AI landscape and the standard ML workflow
Python for AI
- •Python syntax, data types, control flow and functions
- •Lists, dictionaries, comprehensions, classes and modules
- •Virtual environments, Jupyter notebooks and Git basics
- •Lab: Write reusable Python utilities for data tasks
- •Outcome: Write clean Python for data and AI work
Data Handling & Visualisation
- •NumPy arrays and vectorised computation
- •pandas: loading, cleaning, merging and grouping
- •Visualisation with Matplotlib and Seaborn; exploratory data analysis
- •Lab: Explore and clean a real dataset end to end
- •Outcome: Prepare and understand data before modelling
Maths & Statistics for ML
- •Vectors, matrices and matrix operations
- •Probability, distributions, expectation and variance
- •Hypothesis testing, confidence intervals and correlation; gradients and optimisation intuition
- •Lab: Implement gradient descent from scratch
- •Outcome: Understand the mathematics behind learning algorithms
Regression Models
- •Linear regression, assumptions and interpretation
- •Regularisation: ridge, lasso and elastic net; polynomial features
- •Regression metrics: MAE, RMSE and R-squared
- •Lab: Predict house prices with a regression pipeline
- •Outcome: Build and evaluate regression models
Classification Models
- •Logistic regression, k-nearest neighbours, naive Bayes and support vector machines
- •Confusion matrix, precision, recall, F1, ROC-AUC
- •Class imbalance, thresholds and calibration
- •Lab: Build a customer churn classifier and tune the threshold
- •Outcome: Build classifiers and choose metrics for the business
Trees, Ensembles & Boosting
- •Decision trees, splitting criteria and overfitting
- •Bagging and random forests
- •Gradient boosting with XGBoost or LightGBM; feature importance
- •Lab: Compare ensemble models on a tabular dataset
- •Outcome: Use the strongest general-purpose models for tabular data
Feature Engineering & Model Selection
- •Missing values, encoding, scaling and feature creation
- •Pipelines and avoiding data leakage
- •Cross-validation, grid and random search, and Optuna overview
- •Lab: Build a leak-free pipeline with tuning
- •Outcome: Build reliable models that generalise
Unsupervised Learning
- •Clustering with k-means, hierarchical clustering and DBSCAN
- •Dimensionality reduction with PCA and t-SNE or UMAP
- •Anomaly detection basics; recommender system basics
- •Lab: Segment customers and visualise clusters
- •Outcome: Find structure in unlabelled data
Neural Networks & Deep Learning Basics
- •Neurons, layers, activations and loss functions
- •Backpropagation and optimisers
- •Building and training networks with TensorFlow or PyTorch; regularisation
- •Lab: Train a neural network on tabular and image data
- •Outcome: Build and train a basic deep learning model
Computer Vision & NLP Introduction
- •Convolutional neural networks and transfer learning for images
- •Text preprocessing, embeddings and sequence models
- •Transformers and pre-trained models with Hugging Face
- •Lab: Build an image classifier and a text classifier
- •Outcome: Apply deep learning to images and text
Generative AI, Reinforcement Learning & Responsible AI
- •Large language models, prompting and retrieval-augmented generation overview
- •Reinforcement learning concepts: agents, rewards and policies
- •Bias, fairness, explainability with SHAP, privacy and AI governance
- •Lab: Build a simple RAG demo and an explainability report
- •Outcome: Understand modern AI trends and their risks
Deployment, MLOps Basics & Capstone
- •Saving models and serving them with FastAPI
- •Docker basics, experiment tracking with MLflow and monitoring basics
- •Capstone: end-to-end ML project from data to deployed API
- •Portfolio and interview preparation
- •Outcome: A deployed portfolio project and interview readiness
Tools Covered
Projects Included
- House Price Predictor: Build a regression pipeline with feature engineering and regularisation; deliverable is a notebook and evaluation report.
- Customer Churn Classifier: Build and tune a classifier with threshold selection for a business cost trade-off; deliverable is a model and decision memo.
- Customer Segmentation: Cluster customers and profile the segments; deliverable is a notebook and segment descriptions.
- Image and Text Classifiers: Fine-tune a pre-trained image model and a text model; deliverable is two notebooks and a comparison.
- Capstone ML Application: Train, explain and deploy a model with a FastAPI service in Docker; deliverable is a GitHub repository and demo.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Artificial Intelligence (AI & ML) course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Artificial Intelligence (AI & ML).
Certificate of Completion
Artificial Intelligence (AI & ML)
This certifies that
[Your Name]
has successfully completed the
Artificial Intelligence (AI & ML)
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Artificial Intelligence (AI & ML) course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Artificial Intelligence (AI & ML) Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Machine Learning Engineer (entry level), AI Engineer (entry level), Data Scientist (entry level), Data Analyst; support is guidance with the job search, not a guarantee of a job.