Learn from a seasoned professional with 11+ years of experience in Data Science, Machine Learning, Artificial Intelligence, and real-world analytics system development.
Expertise in data preprocessing, exploratory data analysis (EDA), feature engineering, statistical modeling, and building scalable data-driven solutions using Python, SQL, and industry tools.
Specialized in developing supervised and unsupervised machine learning models, time series forecasting, classification, clustering, and model optimization for real business applications.
Strong foundation in neural networks, NLP basics, computer vision concepts, and deploying AI models using modern frameworks such as TensorFlow, Scikit-Learn, and cloud platforms.
Leads end-to-end analytics projects including dashboards, automation pipelines, recommendation systems, fraud detection models, and enterprise-grade AI solutions.
Whether you’re a student, fresher, or working professional, our Data Science (AI & ML) training in Hyderabad equips you with hands-on experience in Python, Machine Learning, data analytics, and real-world projects, helping you become industry-ready and confident for high-paying roles.
* Salary insights are derived from Analytics India Magazine (AIM) Survey, Kaggle Developer Survey, and LinkedIn Data Science reports
₹14 LPA - ₹17 LPA
Analyze and interpret complex data sets to uncover patterns, insights, and trends that drive business decisions.
₹5 LPA - ₹8 LPA
Design and develop machine learning models and algorithms to enable predictive analytics and automate processes.
₹6 LPA - ₹12 LPA
Create data visualizations and reports to communicate insights and support decision-making at the organizational level.
₹12 LPA - ₹14 LPA
Build and maintain the infrastructure required for efficient data storage, processing, and retrieval.
Our structured syllabus guides you from core data science fundamentals to building real-world analytics and machine learning solutions. Each module includes hands-on data projects, data cleaning and feature engineering exercises, model building and evaluation, visualization workflows, capstone projects, and assessments to ensure strong practical mastery and job-ready skills.
Foundations of Data ScienceOverview of the Data Science Lifecycle
Role of a Full-Stack Data Scientist
Tools and Technologies in Full-Stack Data Science
Linear Algebra, Calculus, and Probability Basics
Statistical Distributions and Applications
Python
R Language
Data Preprocessing and Feature Engineering
Data Cleaning and Transformation Techniques
Handling Missing Data, Outliers, and Data Imbalance
Feature Scaling, Encoding, and Selection
Dimensionality Reduction Techniques: PCA, t-SNE, LDA
Machine Learning (ML) and Deep Learning (DL)
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, and kNN
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, and ROC-AUC
eural Networks: Architecture and Training
Deep Learning Frameworks: TensorFlow, PyTorch, and Keras
onvolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Database Management and Big Data
SQL and Relational Databases
Bigdata and Hadoop
Data Visualization and Business Analytics
Creating Visualizations with Matplotlib, Seaborn, Plotly, and Dash
Interactive Dashboards with Power BI
Tableau Basics: Connecting, Building Dashboards, and Storytelling
Fundamentals of Business Analytics
KPI Definition and Business Intelligence
Case Studies: Sales, Marketing, and Operational Analytics
Cloud Computing and Deployment
Basics of Cloud Computing and Its Importance in Data Science
Deploying Machine Learning Models on Azure and AWS
Working with Cloud Databases and Storage Solutions
Building APIs using Flask and FastAPI
Deploying Models with Docker and Kubernetes
MLOps: Monitoring and Maintaining Deployed Models
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