Certificate Artificial Intelligence Training In Hyderabad

Best AI Course In Hyderabad is a powerful combination of analytical skills and intelligent algorithms. It enables individuals to extract meaningful insights from data and build intelligent systems that can learn, adapt, and automate tasks. A well-structured learning path includes both Data Science modules and an AI-focused syllabus, progressing from foundational concepts to advanced applications.

Below is a comprehensive curriculum divided into modules that blend Data Science and Artificial Intelligence Training In Hyderabad seamlessly.

🔹 Module 1: Introduction to Data Science & AI

• What is Data Science?

• What is Artificial Intelligence?

• Differences and connections between Data Science, Machine Learning, and AI

• Real-world applications and career paths

🔹 Module 2: Mathematics & Statistics for DS & AI

• Descriptive statistics and probability theory

• Hypothesis testing and statistical inference

• Linear algebra: vectors, matrices

• Calculus: derivatives and optimization for AI

🔹 Module 3: Programming for Data Science

• Python fundamentals

• Working with libraries: NumPy, Pandas, Matplotlib, Seaborn

• Jupyter Notebooks and Git basics

• Introduction to R (optional)

🔹 Module 4: Data Wrangling and Preprocessing

• Data cleaning techniques

• Handling missing values and outliers

• Data encoding, transformation, and scaling

• Feature engineering

🔹 Module 5: Exploratory Data Analysis (EDA)

• Summary statistics

• Data visualization with Matplotlib and Seaborn

• Correlation analysis

• Identifying trends and patterns in data

🔹 Module 6: Databases and SQL

• Introduction to SQL and relational databases

• Performing queries (SELECT, JOIN, GROUP BY)

• NoSQL overview (MongoDB)

• Connecting databases with Python

🔹 Module 7: Machine Learning (Core AI Module)

• Supervised Learning: Regression, Classification

• Unsupervised Learning: Clustering, Dimensionality Reduction

• Model selection and evaluation: accuracy, precision, recall, ROC-AUC

• Scikit-learn fundamentals

🔹 Module 8: Data Visualization and Storytelling

• Dashboards with Tableau or Power BI

• Plotly and interactive charts

• Communicating insights effectively

• Creating data presentations

🔹 Module 9: Deep Learning (AI Advanced Module)

• Neural networks fundamentals

• Activation functions and backpropagation

• Frameworks: TensorFlow, Keras, PyTorch

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks (RNNs) and LSTMs

🔹 Module 10: Natural Language Processing (AI Advanced Module)

• Text cleaning and tokenization

• Bag of Words and TF-IDF

• Word embeddings: Word2Vec, GloVe, BERT

• Sentiment analysis, chatbots, text classification

🔹 Module 11: AI and Automation in Data Science

• Introduction to Reinforcement Learning

• Intelligent systems and agents

• AI in robotics and process automation

• AI applications in business intelligence and analytics

🔹 Module 12: Model Deployment and MLOps

• Model serialization (Pickle, Joblib)

• Flask or FastAPI for API development

• Docker basics for containerizing AI models

• Introduction to cloud services: AWS, Azure, Google Cloud

• Version control and CI/CD concepts for ML

🔹 Module 13: Ethics, Privacy, and Responsible AI

• Data privacy and protection (e.g., GDPR)

• Bias and fairness in algorithms

• Ethical AI development and governance

• Transparency and explainability (XAI)

🔹 Module 14: Capstone Project

• End-to-end real-world project using Data Science and AI

• Problem definition, data collection, modeling, and deployment

• Presenting results with visualizations and business insights

• Evaluation by peers or mentors