Best AI & Machine Learning Course in Surat
Applied Artificial Intelligence & Machine Learning
Our AI and machine learning course in Surat takes you from Python and data analysis to machine learning models, deep learning, large language models and AI agents you can deploy as real applications. It is a hands-on AI ML course built around projects, taught at our City Light campus and online.
Why learn AI and machine learning now
Companies everywhere are adding prediction, automation and chat assistants to their products, and they need developers who understand both classic machine learning and modern LLM tools. This course teaches both, so you can build a price predictor one week and a document chatbot the next.
Who this AI ML course is for
- Students after 12th, BCA, B.Sc IT and engineering students who want an AI career.
- Developers who want to add machine learning and LLM skills to their work.
- Data analysts who want to move into predictive modelling and AI engineering.
- Beginners with basic computer skills: the course starts with Python for AI.
What you will learn
- Python for AI: Jupyter, Colab and Anaconda, NumPy, Pandas data cleaning, and Matplotlib and Seaborn charts.
- Data analysis: exploratory data analysis, feature engineering, scaling, encoding and PCA.
- Machine learning: Scikit-Learn regression and classification, evaluation metrics, hyperparameter tuning, Random Forests, XGBoost, LightGBM and K-Means clustering.
- Deep learning: neural networks, backpropagation and multi-layer perceptrons in PyTorch or TensorFlow/Keras.
- NLP and vector databases: embeddings, semantic search, Pinecone, ChromaDB or Weaviate.
- Large language models: integrating OpenAI, Gemini and Llama APIs, prompt engineering, structured JSON output and when to fine-tune.
- AI agents and RAG: LangChain and LlamaIndex, chat-with-your-data pipelines, tool-using agents and multi-agent systems.
- Deployment: Streamlit or Gradio apps, FastAPI services, Docker and cloud deployment.
Projects you will build
You solve industry-style problems such as customer churn prediction, price forecasting and anomaly detection, then finish with a capstone AI application, for example an automated customer support agent, a real estate price predictor or an intelligent document analyser, deployed with an API and a web interface.
AI and machine learning course fees
The course is available as a basic track of 4-5 months and an advanced track of 1 year. The listed fee is ₹50,000; call +91 95109 90292 to confirm the fee and duration of the track that fits your goal, or book a free demo class at our City Light campus.
Career after the course
Graduates apply for roles such as Machine Learning Engineer, AI Developer, Data Scientist and AI Integration Developer, or add AI features to their current development work. Our placement assistance covers your portfolio, resume and technical interview preparation.
Related AI courses
Want to focus on prompt engineering, custom GPTs and AI agents without heavy maths? See our generative AI course. Prefer statistics, forecasting and analytics? Compare the data science course and the data analytics course.
Duration: 4-12 Months · Fees: ₹50,000
Course Curriculum
Python for Data Science & AI Foundations
- Setting up the AI development environment (Jupyter Notebooks, Google Colab, Anaconda).
- Python crash course for AI: Data structures, functions, and object-oriented programming.
- Mastering NumPy for high-performance numerical computing and matrix operations.
- Data manipulation and cleaning with Pandas: Handling missing data, merging, and filtering.
- Data visualization essentials using Matplotlib and Seaborn to understand data distributions.
Exploratory Data Analysis (EDA) & Feature Engineering
- Techniques for understanding structured vs. unstructured data.
- Identifying correlations, outliers, and statistical significance in datasets.
- Feature engineering: Encoding categorical variables, scaling, and normalization.
- Dimensionality reduction techniques (PCA) to optimize dataset performance.
- Splitting data into training, validation, and testing sets to prevent data leakage.
Applied Predictive Modeling (Machine Learning)
- Introduction to Scikit-Learn and the traditional Machine Learning pipeline.
- Supervised Learning (Regression): Predicting continuous values (Linear Regression, Ridge/Lasso).
- Supervised Learning (Classification): Predicting categories (Logistic Regression, SVMs, KNN).
- Model evaluation metrics: Accuracy, Precision, Recall, F1-Score, and the Confusion Matrix.
- Hyperparameter tuning using Grid Search and Random Search.
Advanced ML: Ensemble Methods & Unsupervised Learning
- Decision Trees and the power of ensemble learning.
- Building high-accuracy models with Random Forests and Gradient Boosting (XGBoost/LightGBM).
- Unsupervised Learning: Discovering hidden patterns using K-Means Clustering.
- Solving real industry problems: Customer churn prediction, price forecasting, and anomaly detection.
- Saving and loading trained models using Pickle and Joblib.
Deep Learning & Neural Network Foundations
- Understanding the shift from traditional ML to Deep Learning.
- Introduction to PyTorch or TensorFlow/Keras frameworks.
- The architecture of a Neural Network: Perceptrons, hidden layers, and activation functions (ReLU, Sigmoid).
- Forward propagation, backpropagation, and understanding loss functions.
- Building a basic Multi-Layer Perceptron (MLP) for complex classification tasks.
NLP & Vector Databases for Modern AI
- Introduction to Natural Language Processing (NLP) and text tokenization.
- Understanding word embeddings and semantic search.
- Generating vector embeddings using OpenAI and Hugging Face embedding models.
- Setting up and querying Vector Databases (e.g., Pinecone, ChromaDB, or Weaviate).
- Implementing highly accurate semantic search for large document structures.
Integrating Large Language Models (LLMs)
- Overview of the LLM landscape: OpenAI (GPT-4), Google (Gemini), and Open-Source (Llama 3).
- Obtaining API keys and securely integrating LLM APIs into Python applications.
- Advanced Prompt Engineering: Few-shot prompting, chain-of-thought, and system prompts.
- Controlling LLM output formatting (JSON parsing) for seamless application integration.
- Fine-tuning fundamentals: When to fine-tune vs. when to use prompt engineering.
Building AI Agents & RAG Applications
- Introduction to LangChain and LlamaIndex for orchestrating complex AI workflows.
- Building Retrieval-Augmented Generation (RAG) pipelines to let LLMs "chat" with your private data (PDFs, databases).
- Giving AI tools: Creating autonomous AI Agents that can search the web, execute code, or query SQL databases.
- Managing agent memory and conversational context over long sessions.
- Solving complex problems with multi-agent architectures.
Model Deployment & MLOps
- Transitioning from Jupyter Notebooks to production-ready Python scripts.
- Building interactive web interfaces for AI models instantly using Streamlit or Gradio.
- Wrapping predictive models and LLM agents into RESTful APIs using FastAPI.
- Containerizing the AI application and its dependencies using Docker.
- Deploying the AI API to cloud platforms (AWS, Render, or Hugging Face Spaces).
Industry Capstone Project & Career Placement
- Building a full-scale, portfolio-ready AI application (e.g., an automated customer support agent, a real estate price predictor, or an intelligent document analyzer).
- Best practices for version controlling data and models (Git/DVC).
- Preparing for technical data science and AI engineering interviews.
- Navigating Surat's evolving tech landscape and positioning yourself as a specialized AI developer.
Why Choose Extrabits for Applied Artificial Intelligence & Machine Learning Training?
At Extrabits Academy, we provide the best AI & Machine Learning Course in Surat, offering more than just theory - we provide an opportunity to enhance your career with practical knowledge and live project experience.
Gain insights from professionals with years of real-world experience.
Work on live projects and build a portfolio that stands out.
Choose from weekend or weekday batches to suit your schedule.
Master core concepts and advanced techniques in modern technology.
Apply your skills to solve real business problems through case studies.
Earn a recognized certificate to boost your career opportunities.
Student Reviews
Rated 4.9/5 ★ from 109 Google reviews
Google review · Oct 2025
★★★★★
"I had a great experience at ExtraBits. The teacher is extremely supportive, friendly, helpful, and always encourages students to ask questions. Their way of explaining concepts is simple and easy to understand. They’re also very patient and make sure that every student is comfortable with the topic before moving on. Overall, it’s a very positive and motivating environment for learning.🫶🏻"
Google review · Oct 2024
★★★★★
"In my time at extrabits , I witnessed a remarkable improvement in my IT skills and a boost in my confidence. The results speak for themselves, with many students excelling in exams and securing rewarding positions"
Google review · Oct 2022
★★★★★
"One of the best coaching experience, unique approach of adaptive method and faculty is just wow. IT students should definately join here undoubtedly. .."
FAQs about the AI & Machine Learning Course in Surat
What is the fee for the AI and machine learning course in Surat?
The listed fee for the Applied AI and Machine Learning course at ExtraBits Academy is ₹50,000. Call +91 95109 90292 to confirm the fee and duration of the basic or advanced track.
How long is the AI ML course?
There is a basic track of 4-5 months and an advanced track of 1 year. Our counsellors help you choose based on your background and goals.
Do I need coding or maths experience to learn AI?
No prior AI experience is needed. The course starts with Python for AI and teaches the statistics and data skills you need along the way. Basic computer skills are enough to begin.
Does the course cover ChatGPT, LLMs and AI agents?
Yes. You integrate OpenAI, Gemini and open-source LLM APIs, build RAG pipelines that chat with your own documents, and create tool-using AI agents with LangChain and LlamaIndex.
What is the difference between the AI ML course and the generative AI course?
The AI ML course covers machine learning, deep learning and LLM engineering in depth. The generative AI course focuses on prompt engineering, custom GPTs, agent frameworks and automation, with less maths and model training.
Is placement assistance included?
Yes. We help you build an AI project portfolio, prepare your resume and practise technical interviews.
Where are the AI classes in Surat held?
At our campus on the 3rd floor of Agrasen Shopping Point, City Light Road, Athwa, Surat 395007. Online batches are also available across Surat.
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