About This Program
This program bridges the gap between AI theory and business application. You won't just learn Python ML libraries — you'll learn how to apply them to real business problems: training an invoice extraction model on actual invoice data, building a demand forecasting pipeline from ERP sales history, deploying an LLM-powered chatbot connected to a CRM. The curriculum emphasizes the full ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring.
What You'll Be Able to Do
You will build and deploy at least 3 production-ready ML models — document intelligence, demand forecasting, and customer churn prediction — all integrated with business systems.
Who Should Attend
- Software developers and data analysts wanting to add practical AI/ML skills
- ERP consultants who want to sell and deliver AI integration projects
- Business intelligence professionals transitioning into AI
- Computer Science graduates pursuing careers in applied AI
- Business leaders who want to understand what AI can (and can't) do
Prerequisites
Basic Python programming. High-school level mathematics (statistics, probability). No prior ML or AI experience required.
Available Formats
Curriculum Breakdown
4 modules — 7 Weeks (140 Hours) of hands-on, project-based learning.
Module 1: AI/ML Foundations & Python for Data Science
Python ML ecosystem: NumPy, Pandas, Matplotlib, Scikit-learn. Exploratory Data Analysis (EDA). Data cleaning and preprocessing. Feature engineering. Train-test split and cross-validation. Model evaluation: accuracy, precision, recall, F1, ROC-AUC. Understanding bias, variance, overfitting, underfitting.
Module 2: Supervised & Unsupervised Learning
Regression: Linear, Decision Tree, Random Forest — for sales forecasting. Classification: Logistic Regression, SVM, XGBoost — for churn prediction and lead scoring. Clustering: K-Means, DBSCAN — for customer segmentation. Time series: ARIMA, Prophet — for demand forecasting from ERP data.
Module 3: Deep Learning & NLP
Neural network fundamentals with TensorFlow/Keras. CNN for document image classification. NLP with transformers: sentiment analysis, NER, text classification. Document intelligence: invoice/PO data extraction with OCR + ML. Introduction to LLMs and prompt engineering.
Module 4: MLOps — Deploying Models to Production
Model serialization. Building REST APIs for inference with FastAPI. Containerizing ML models with Docker. Deploying to Azure ML / AWS SageMaker. Model monitoring: data drift, concept drift. Retraining strategies. Integrating ML with ERP systems (BC/ERPNext). Building business impact dashboards.
Why Train With Dhaara Tech Academy?
Our programs are designed to produce capable practitioners — not just certificate collectors.
Practitioner-Led
Your instructor deployed this technology for a real client this week.
Project-Based
You build something real in every module. Portfolio-worthy output.
Respected Certification
Our certificates represent demonstrated capability, not attendance.
Post-Training Support
4 weeks email support + community forum + optional mentorship.
Enroll in This Program
Interested in "Artificial Intelligence & Machine Learning — Applied Training for Business Applications"? Fill out the form and we'll send you the detailed curriculum, upcoming batch dates, and investment details within 24 hours.