Data Science with AI/ML Course in Ahmedabad

The Data Science with AI/ML course helps learners understand how data, machine learning, and artificial intelligence work together in real-world applications.

To begin with, the program explains core concepts using simple language and practical examples. As a result, even beginners can follow the learning process with confidence.

In addition, the course focuses on hands-on learning through real datasets and live demonstrations. For example, students practice data analysis and model building based on real industry scenarios.

Moreover, Python, statistics, machine learning, deep learning, NLP, and computer vision are taught step by step. Because of this, learners build strong technical foundations without feeling overwhelmed.

Finally, the course includes internship support, certification, and career guidance. Therefore, students are well prepared for interviews and professional roles in Data Science and AI.

Detailed Course Syllabus – Data Science with AI/ML

Module 1: Introduction to Data Science & AI/ML

• What is Data Science?
• Roles of Data Scientist, ML Engineer & AI Engineer
• Data Science lifecycle
• Overview of Artificial Intelligence & Machine Learning
• Types of Machine Learning
• Real-world industry use cases

Module 2: Python for Data Science

• Python basics and environment setup
• Data types, loops, and functions
• NumPy and Pandas for data handling
• Data cleaning and preprocessing
• Working with real datasets

Module 3: Statistics & Mathematics

• Descriptive statistics
• Probability and data distributions
• Hypothesis testing concepts
• Correlation and covariance
• Linear algebra fundamentals
• Gradient descent basics

Module 4: Data Analysis & Visualization

• Exploratory Data Analysis (EDA)
• Data visualization using Matplotlib, Seaborn & Plotly
• Dashboard fundamentals
• Data storytelling techniques

Module 5–7: Machine Learning

• Machine Learning workflow and feature engineering
• Regression and classification algorithms
• KNN, Decision Trees, and Random Forest
• Support Vector Machines (SVM)
• Clustering and dimensionality reduction

Module 8–10: Deep Learning, NLP & Computer Vision

• Artificial Neural Networks (ANN)
• TensorFlow and Keras fundamentals
• Natural Language Processing and text analytics
• Chatbots and AI models
• OpenCV and Convolutional Neural Networks (CNN)
• Image classification and object detection

Module 11–14: Advanced AI & Real-World Projects

• Generative AI and AI APIs
• Big Data concepts and Cloud platforms (AWS / GCP)
• End-to-end Machine Learning and AI projects
• Internship and certification
• Resume building and interview preparation

Who Should Join This Course?

This course is ideal for students, fresh graduates, working professionals, and career switchers.

Most importantly, no prior technical experience is required. Therefore, the training starts from the basics and gradually moves to advanced concepts.