Best Data Science & Predictive Modeling Course in Surat

Turn data into actionable decisions.

Learn Python, R, and statistical modeling with a heavy focus on AI automation to land high-paying Data Scientist roles in India's booming tech sector.

Duration: 12 Months

Course Curriculum

Module 1: Python, NumPy & Pandas for AI/ML

Python Programming Foundations
Python basics, data structures, functions, file handling, and OOP basics.
AI/ML Development Environment
Jupyter Notebook, Google Colab, Anaconda, and VS Code setup.
NumPy for Numerical Computing
Arrays, indexing, slicing, matrix operations, and numerical calculations.
Pandas for Data Handling
DataFrame, Series, reading datasets, filtering, grouping, merging, and aggregation.
Data Cleaning
Handling missing values, duplicate values, incorrect data types, and messy datasets.

Module 2: Mathematics & Statistics for Machine Learning

Mathematics for ML
Linear algebra basics, vectors, matrices, and gradient descent introduction.
Descriptive Statistics
Mean, median, mode, variance, standard deviation, and data distribution.
Probability & Data Relationships
Probability basics, normal distribution, correlation, and outlier understanding.

Module 3: Data Visualization, EDA & Feature Engineering

Data Visualization
Matplotlib, Seaborn, charts, graphs, and visual storytelling.
Exploratory Data Analysis (EDA)
Data distribution, patterns, trends, outliers, and business insights.
Feature Engineering
Feature selection, feature creation, categorical encoding, scaling, and normalization.
Data Preprocessing
Train-test split, data leakage prevention, and preprocessing workflow.

Module 4: Supervised Machine Learning

Regression Models
Linear Regression, Multiple Linear Regression, Ridge Regression, and Lasso Regression.
Classification Models
Logistic Regression, KNN, SVM, Naive Bayes, and Decision Trees.
Model Performance Metrics
Regression and classification evaluation metrics.
Projects
- House Price Prediction
- Salary Prediction
- Customer Churn Prediction
- Loan Approval Prediction

Module 5: Model Evaluation & Hyperparameter Tuning

Model Evaluation
Training, testing, validation, cross-validation, and model comparison.
Hyperparameter Tuning
GridSearchCV, RandomizedSearchCV, and parameter optimization.
Model Saving & Reuse
Pickle, Joblib, and reusable model files.
ML Workflow
Building complete machine learning pipelines.

Module 6: Unsupervised Learning & Clustering

Clustering Techniques
K-Means Clustering, Elbow Method, and Silhouette Score.
Dimensionality Reduction
Principal Component Analysis (PCA).
Anomaly Detection
Basic anomaly detection concepts.
Project
- Customer Segmentation using RFM and K-Means.

Module 7: Advanced Machine Learning & Ensemble Models

Tree-Based Models
Decision Trees and advanced tree-based concepts.
Ensemble Learning
Bagging, Boosting, and model combination techniques.
Advanced ML Models
Random Forest, Gradient Boosting, XGBoost, and LightGBM.
Model Interpretation
Feature importance and explainable AI concepts.
Projects
- Fraud Detection
- Credit Risk Prediction
- Business Forecasting Problems

Module 8: Deep Learning, Computer Vision & Time Series

Neural Network Foundations
Neural networks, activation functions, loss functions, and backpropagation.
Deep Learning Frameworks
PyTorch and TensorFlow/Keras.
Computer Vision
Image data, CNNs, image classification, and transfer learning.
Time Series Forecasting
Trend, seasonality, and forecasting models.
Projects
- Image Classification
- Sales Forecasting

Module 9: NLP, Embeddings & Semantic Search

Natural Language Processing
Text cleaning, tokenization, TF-IDF, sentiment analysis, and text classification.
Transformer Models
Introduction to Transformers and Hugging Face.
Embeddings
Word embeddings, sentence embeddings, and semantic similarity.
Vector Databases
ChromaDB, FAISS, Pinecone overview, and vector search.
Project
- AI-Powered Document Search Engine.

Module 10: AI App Development, FastAPI & RAG

AI App Development
Streamlit applications and Gradio demos.
API Development
FastAPI for ML and AI applications.
Retrieval-Augmented Generation (RAG)
Document loading, chunking, embeddings, vector databases, retrieval, and PDF Q&A.
Why LLMs need external data.
RAG architecture and workflow.
LLM Application Development
Prompt engineering, structured outputs, API integration, and chatbot workflows.
Projects
- Chat with PDF
- AI Chatbot
- AI Document Assistant

Module 11: MLOps, LLMOps & AI System Design

MLOps & LLMOps
Experiment tracking, model versioning, and AI workflow management.
Deployment
Docker, Render, and Hugging Face Spaces.
Production Architecture
API architecture, vector databases, cost optimization, latency, and security.
Monitoring & Maintenance
Logging, monitoring, and production readiness checklist.
Capstone Project
- End-to-end AI application development and deployment.

Why Choose Extrabits for Data Science & Predictive Modeling Course Training?

At Extrabits Academy, we provide the best Data Science & Predictive Modeling Course course in Surat, offering more than just theory — we provide an opportunity to enhance your career with practical knowledge and live project experience.

🎓 Learn from Industry Experts

Gain insights from professionals with years of real-world experience.

💻 Hands-On, Practical Learning

Work on live projects and build a portfolio that stands out.

📅 Flexible Learning Options

Choose from weekend or weekday batches to suit your schedule.

🏗️ Build a Strong Foundation

Master core concepts and advanced techniques in modern technology.

💼 Real-World Projects

Apply your skills to solve real business problems through case studies.

🏆 Get Certified

Earn a recognized certificate to boost your career opportunities.