Applied AI & Machine Learning

Python with Machine Learning

A rigorous, practical program designed to take you from foundational Python programming to designing, training, evaluating, and deploying production-grade Machine Learning models.

4 Months Only 25 Seats Fri, Sat & Sun, 7:00 – 9:00 PM (2 Hours) ₹7,000  ₹5,000
Enroll Now → View Curriculum ↓

* Started from 9th October (Fri, Sat & Sun) • Demo was on 28, 29, 30 Oct

EuroKids PreSchool Near Maratha Hotel in Narhe, Pune

"

Without big data, you are blind and deaf and in the middle of a freeway.

— Geoffrey Moore

Tools & Libraries Covered

Python 3.12
NumPy
Pandas
Scikit-Learn
PyTorch
FastAPI
Git & GitHub

Program Curriculum

From Python foundations to production machine learning models.

01

Advanced Python & Software Engineering Craft

▼
  • Python InternalsUnderstanding memory model, mutability, references, namespaces, garbage collection, and execution lifecycle
  • Advanced OOP & PatternsClasses, inheritance, mixins, dunder methods, properties, and encapsulation in Python
  • Functional ParadigmFirst-class functions, closures, decorators, generators, and memory-efficient iterators
  • Environment & ToolingVirtual environments (uv / venv), packaging structure, linters, and Git workflow
02

Mathematics for ML & Scientific Computing (NumPy & Pandas)

▼
  • Linear Algebra in CodeVectors, matrices, dot products, transpositions, eigenvalues, and matrix factorization
  • Optimization & CalculusDerivatives, partial derivatives, gradients, vector calculus, and Gradient Descent from scratch
  • NumPy MasteryN-dimensional arrays, vectorization, SIMD operations, broadcasting rules, and numerical stability
  • Pandas for Data WranglingDataFrames, multi-indexing, aggregation, grouping, merges, joins, and time-series manipulation
03

Exploratory Data Analysis & Feature Engineering

▼
  • Data Cleaning & ImputationHandling missing values, statistical imputations, IQR outlier detection, and data sanity checks
  • Data VisualizationCrafting diagnostic statistical plots with Matplotlib and Seaborn, and correlation heatmaps
  • Feature TransformationOne-hot encoding, target encoding, standard scaling, MinMax, Box-Cox transforms, and power transforms
  • Dimensionality ReductionPrincipal Component Analysis (PCA) mathematical theory, implementation, and scree plots
04

Supervised Learning: Classical Algorithms in Depth

▼
  • Regression ModelsOrdinary Least Squares, Ridge (L2), Lasso (L1), and ElasticNet regularization with Scikit-Learn
  • Classification ModelsLogistic Regression, Sigmoid activation, Maximum Likelihood Estimation, and Decision Boundaries
  • Tree-Based & EnsemblesDecision Trees, Gini Impurity, Random Forests, Bagging, and Gradient Boosting (XGBoost/LightGBM)
  • Support Vector MachinesHyperplanes, maximal margins, support vectors, and non-linear kernel transformations (RBF/Polynomial)
  • Evaluation & ValidationK-Fold Cross-Validation, Precision, Recall, F1-Score, ROC-AUC curves, and Confusion Matrices
05

Unsupervised Learning & Deep Learning Foundations

▼
  • Clustering AlgorithmsK-Means clustering, Elbow method, Silhouette analysis, and DBSCAN density-based clustering
  • Neural Network EssentialsPerceptrons, Multi-Layer Perceptrons (MLPs), and non-linear activation functions (ReLU, GELU, Softmax)
  • PyTorch FundamentalsTensors, computational graphs, Autograd engine, and building custom nn.Module classes
  • Training PipelineLoss functions (MSE, Cross-Entropy), forward passes, backpropagation, and Adam/SGD optimizers
06

Model Deployment, Production ML & Capstone

▼
  • Model SerializationExporting production artifacts via Joblib, Pickle, and ONNX runtime formats
  • REST API ServingBuilding high-performance inference microservices using FastAPI and Pydantic schemas
  • Interactive DashboardsRapidly building live prediction dashboards and model inspectors with Streamlit
  • Capstone ShowcaseDeliver, deploy, and present an end-to-end Machine Learning pipeline project to mentors

Applied Portfolio

Projects You Will Build & Deploy.

Every concept and algorithm is applied hands-on to build production-grade machine learning applications.

Production ML

Customer Churn & CLV Prediction Engine

Enterprise ML Pipeline

A complete end-to-end churn prediction engine with automated feature engineering pipelines, XGBoost modeling, SHAP explainability values, and REST API deployment.

Python Scikit-Learn XGBoost FastAPI
Full Stack Machine Learning →
Deep Learning

Defect Detection & Vision Classifier

Deep Learning in Production

A Convolutional Neural Network (CNN) built and trained from scratch with PyTorch, automated data augmentations, holdout evaluation benchmarks, and live Streamlit demo.

PyTorch Computer Vision Streamlit CNN
Deep Learning Pipeline →

Build. Train. Deploy.

Python with Machine Learning

Start your journey from writing clean, vectorized Python code to training, evaluating, and deploying real Machine Learning models in production. Sessions on Fri, Sat & Sun (₹5,000 for complete 4 months).

Reserve Your Seat via WhatsApp — ₹5,000 →