Data Science with AI Course in Jaipur
Kickstart your career in the world of Data Science and Artificial Intelligence with our industry-focused course in Jaipur. This program is ideal for beginners and professionals looking to gain expertise in Python, Machine Learning, Deep Learning, and data-driven AI applications. Learn to extract insights, build predictive models, and work on real-world projects guided by experienced instructors. Master the skills required to become a data scientist and stay ahead in the AI-powered future. Enroll now and step into one of the most in-demand career fields today.
- Overview
- Curriculum
This Data Science with AI tutorial is designed to help you learn quickly and effectively. Whether you're a beginner or aiming to advance your career, our course covers essential tools and technologies like Python, Machine Learning, Deep Learning, and AI-based applications through hands-on exercises and real-world projects.
- Complete AI ML Course
- Full Lifetime Access
- Certificate of Completion
- Projects in Web Development with Python
- Basics of Data Science with Python
- 7-Day Free Trial
You’ll not only understand the core principles of data analytics, but also learn to apply them in real business scenarios. By the end of the course, you’ll work on multiple industry-relevant projects involving data cleaning, visualization, dashboard creation, and generating actionable insights — equipping you with skills that are highly valued in today’s data-driven job market.
- Introduction to Data Science, ML & AI
- Python Basics (Variables, Data Types, Operators)
- Control Structures and Functions in Python
- Data Structures (Lists, Tuples, Dictionaries, Sets)
- Working with NumPy for Numerical Computation
- Data Manipulation using Pandas
- Data Visualization using Matplotlib & Seaborn
- Exploratory Data Analysis (EDA) on Real Datasets
- SQL Basics – Select, Join, Group By, Subqueries
- Connecting Python with SQL for Data Extraction
- Introduction to Machine Learning Concepts
- Supervised Learning – Linear & Logistic Regression
- Model Evaluation Metrics – Accuracy, Confusion Matrix
- Decision Trees and Random Forest
- Unsupervised Learning – K-Means Clustering
- Project: Predictive Model using Real Dataset
- Introduction to Neural Networks and Deep Learning
- Working with TensorFlow and Keras
- Image Classification using CNNs
- Natural Language Processing (NLP) Basics
- Model Deployment using Flask
- Capstone Project: End-to-End Data Science & AI Project
- Resume Preparation & Interview Guidance
