Python Data Science & Machine Learning Online Training
Complete Data Science & ML Online Training — Applied Math/Stats, NumPy, Pandas, Scikit-Learn, PyTorch Deep Learning, NLP, Transformers, MLOps & Capstone.
Technologies You Will Master
Everything Included: Complete Syllabus, Live Classes & Capstone
This focused 6-week (60 hours) live online training covers 100% of the Python Data Science & Machine Learning Online Training syllabus from core fundamentals to production architecture — culminating in a mentor-reviewed Hands-On Capstone Project you can showcase directly on GitHub and in technical interviews.
Course Overview & Architectural Focus
### 💻 Complete Live Online Training + Hands-On Capstone (₹4,999 All-Inclusive)
Python Data Science & Machine Learning Online Training is a focused, practical **6-Week (60-Hour) Live Online Training Course** engineered to take you through the **complete end-to-end syllabus** without bloated theory or overpriced ₹50,000+ career bundles. Every concept is coded live with industry mentors and reinforced through hands-on labs.
### 🚀 What Is Included in Your ₹4,999 Online Training
- **Complete Structured Syllabus (9 Modules)**: Step-by-step live online classes covering fundamentals, internal mechanics, performance best practices, and production patterns.
- **Hands-On Capstone Project**: Build, test, document, and deploy a real-world GitHub portfolio project reviewed by your trainer.
- **Topic-Wise Mock Tests & AI Viva**: Reinforce every lesson with timed MCQ diagnostics and interactive AI technical viva practice.
- **Direct Trainer Doubt Clearing & Callback Guidance**: Get personalized batch timing support (morning/evening/weekend) and 1-on-1 doubt resolution.
### 🎯 Who Should Join
Students, analysts, Python programmers, and software engineers building strong foundations in Data Science, Classical ML, PyTorch, and MLOps.
Technologies & Toolchain Mastered
Production-level tools and frameworks practiced during live online coding sessions and capstone development.
Structured Learning Path (4 Phases)
Week-by-week progression taking you from core foundations to your completed Capstone Project.
Math Foundations, NumPy & Pandas Data Wrangling
Clean, transform, and explore structured datasets using vectorized operations and statistical rigor.
Classical Machine Learning with Scikit-Learn
Build end-to-end feature pipelines, hyperparameter tuning grids, and calibrated ensemble models.
Deep Learning, NLP & Transformer Internals
Train deep neural architectures with autograd, embeddings, tokenization, and attention mechanisms.
MLOps Deployment & Predictive ML Capstone
Track experiments, monitor data drift, and deploy a real-time ML inference API Capstone.
Complete Course Syllabus (9 Modules)
Every module includes live coding labs, 20+ topic mock tests per lesson, and AI technical viva practice.
AI taxonomy, machine learning paradigms, end-to-end ML workflows, classical algorithms, and evaluation metrics.
Hands-on concept explanation, live coding examples, and practice exercises for AI Taxonomy: Artificial Intelligence, Machine Learning, Deep Learning & Generative AI Differences.
Hands-on concept explanation, live coding examples, and practice exercises for Machine Learning Paradigms: Supervised, Unsupervised, Semi-Supervised & Reinforcement Learning.
Hands-on concept explanation, live coding examples, and practice exercises for End-to-End ML Workflow: Data Collection, Cleaning, Feature Engineering, Training, Validation & Deployment.
Hands-on concept explanation, live coding examples, and practice exercises for Core ML Algorithms & Evaluation: Regression, Decision Trees, Random Forests, Clustering, Accuracy, F1 & ROC-AUC.
Linear algebra, matrix operations, probability, Bayes theorem, statistics, calculus, and gradient descent for AI systems.
Hands-on concept explanation, live coding examples, and practice exercises for Linear Algebra for AI: Vectors, Matrices, Matrix Multiplication, Dot Products, Transpose, Norms & Dimensions.
Hands-on concept explanation, live coding examples, and practice exercises for Probability & Bayes Theorem: Conditional Probability, Bayes Rule, Probability Distributions & Expected Values.
Hands-on concept explanation, live coding examples, and practice exercises for Statistics & Distributions: Mean, Median, Variance, Standard Deviation, Correlation & Covariance.
Hands-on concept explanation, live coding examples, and practice exercises for Calculus & Optimization: Derivatives, Gradients, Partial Derivatives & Gradient Descent Optimization.
Multidimensional arrays, vectorization, Pandas Series/DataFrames, data cleaning, aggregation, and AI data preprocessing.
Hands-on concept explanation, live coding examples, and practice exercises for NumPy Core: Multidimensional Arrays, Shapes, Indexing, Slicing, Broadcasting & Vectorized Math.
Hands-on concept explanation, live coding examples, and practice exercises for Pandas DataFrames: Reading CSV/JSON/Excel, Filtering, Grouping, Joining, Aggregations & Missing Values.
Hands-on concept explanation, live coding examples, and practice exercises for Data Transformation for AI: Feature Normalization, One-Hot Encoding, Outlier Detection & Dataset Preparation.
Preprocessing, supervised models, hyperparameter tuning, model evaluation, and practical candidate skill classification.
Hands-on concept explanation, live coding examples, and practice exercises for Scikit-Learn Pipelines: Train/Test Split, Feature Scaling (StandardScaler/MinMax), Encoders & Pipelines.
Hands-on concept explanation, live coding examples, and practice exercises for Model Training: Linear Models, Decision Trees, Random Forests, Gradient Boosting, SVM & K-Means.
Hands-on concept explanation, live coding examples, and practice exercises for Hyperparameter Tuning & Cross-Validation: GridSearchCV, RandomizedSearchCV, Confusion Matrix & Classification Reports.
Hands-on concept explanation, live coding examples, and practice exercises for Practical Project: Candidate Skill Classification & Resume Match Recommendation Engine.
Neural networks, perceptrons, activation functions, loss functions, backpropagation, and PyTorch with CUDA acceleration.
Hands-on concept explanation, live coding examples, and practice exercises for Artificial Neural Networks: Neurons, Multi-Layer Perceptrons, Weights, Biases & Forward Propagation.
Hands-on concept explanation, live coding examples, and practice exercises for Activation & Loss Functions: Sigmoid, Tanh, ReLU, GELU, Softmax, MSE, Binary & Categorical Cross-Entropy.
Hands-on concept explanation, live coding examples, and practice exercises for Backpropagation & Optimizers: Gradient Computation, Chain Rule, SGD, Adam, AdamW & Learning Rate Schedulers.
Hands-on concept explanation, live coding examples, and practice exercises for PyTorch Core: Tensors, CUDA GPU Acceleration, Autograd Engine & Building Custom nn.Module Networks.
Text preprocessing, tokenization, stemming/lemmatization, POS tagging, NER, TF-IDF, word embeddings, and text classification.
Hands-on concept explanation, live coding examples, and practice exercises for Text Preprocessing: Tokenization, Stop Words, Stemming, Lemmatization, POS Tagging & Named Entity Recognition (NER).
Hands-on concept explanation, live coding examples, and practice exercises for Classical Text Representations: Bag of Words (BoW), TF-IDF Vectors, N-Grams & Word2Vec/GloVe Embeddings.
Hands-on concept explanation, live coding examples, and practice exercises for Core NLP Tasks: Text Classification, Sentiment Analysis, Information Extraction & Text Summarization.
Transformer architecture, encoder-decoder, self-attention, multi-head attention, positional encodings, and model families.
Hands-on concept explanation, live coding examples, and practice exercises for Transformer Architecture: Encoder, Decoder, Self-Attention, Feed-Forward Networks & Residual Connections.
Hands-on concept explanation, live coding examples, and practice exercises for Attention Mechanisms: Query, Key, Value Matrices, Scaled Dot-Product Attention & Multi-Head Attention.
Hands-on concept explanation, live coding examples, and practice exercises for Positional Encoding & Normalization: Sinusoidal Positional Embeddings, Learned Encodings, LayerNorm & RMSNorm.
Hands-on concept explanation, live coding examples, and practice exercises for Model Architectures: Encoder-only (BERT), Decoder-only (GPT), and Encoder-Decoder (T5) Comparison.
MLOps lifecycle, data pipelines, model registries, experiment tracking, model deployment, and continuous monitoring.
Hands-on concept explanation, live coding examples, and practice exercises for MLOps Principles & Culture: Bridging Data Science and Operations for Production ML Applications.
Hands-on concept explanation, live coding examples, and practice exercises for Model Registry & Experiment Tracking: Model Versioning, Artifact Logging, Lineage Tracking & Model Registries.
Hands-on concept explanation, live coding examples, and practice exercises for Continuous Training & Deployment: Automated Validation, Deployment Strategies & Continuous Model Monitoring.
Engineer features, train & compare Scikit-Learn + PyTorch models, track metrics, and serve real-time predictions via FastAPI & Docker.
Build leak-free ColumnTransformer pipelines handling class imbalance, outliers, and categorical encoding.
Benchmark gradient-boosted trees vs PyTorch tabular networks with precision-recall threshold calibration.
Containerize the inference endpoint with Pydantic schemas and defend model evaluation choices in AI Viva.
Hands-On Capstone & Production Projects (1)
Build, test, and deploy real production repositories reviewed by your trainer for your engineering portfolio.
Data Science & ML Capstone — FinTech Credit Risk & Customer Churn Prediction Engine
Build a production-grade ML pipeline performing automated EDA, feature engineering, ensemble + neural model training, SHAP explainability, and sub-30ms FastAPI real-time risk scoring.
🎯 Who Should Join
Students, analysts, Python programmers, and software engineers building strong foundations in Data Science, Classical ML, PyTorch, and MLOps.
📋 Prerequisites
Basic Python syntax familiarity (variables, loops, functions). All math, NumPy, Pandas, and ML concepts are taught from the ground up.
Frequently Asked Questions
Key details about course fees, live batch schedules, syllabus depth, capstone review, and certificate verification.
What is the fee and duration for Python Data Science & Machine Learning Online Training? ↓
Python Data Science & Machine Learning Online Training has an all-inclusive fee of ₹4,999 for 6 weeks (60 hours at 10 hours/week) of live instructor-led online classes, complete module-by-module lab source code, topic mock tests, AI viva practice, and capstone mentorship.
Does Python Data Science & Machine Learning Online Training cover the complete syllabus and a real capstone project? ↓
Yes. You cover all 9 structured modules mastering Python Data Stack, NumPy & Pandas, Applied Statistics & Linear Algebra, Scikit-Learn ML, PyTorch Deep Learning, and build the hands-on capstone project (Data Science & ML Capstone — FinTech Credit Risk & Customer Churn Prediction Engine) with mentor code review and GitHub portfolio deployment.
How do I enquire or connect with the trainer before joining a batch? ↓
Click the "Enquire / Request Trainer Callback" button on this course page and submit your contact details. Our trainer will call you directly to walk through the complete syllabus, upcoming live batch timings, and capstone architecture.
Are topic-wise mock tests, AI interviews, and a completion certificate included? ↓
Yes. Every lesson includes 20+ calibrated topic mock tests plus live AI voice technical viva practice, and graduates receive a tamper-proof QR-verifiable Course Completion Certificate.
Enquire for Live Online Batch (₹4,999)
Get a direct call from the course trainer with upcoming batch timings, full syllabus PDF, and capstone overview.
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