Program Duration

6 Months (Monday to Friday, 2 Hours/Day)

Classes & Hours

Approximately 130 Classes & ~260 Total Teaching Hours

Course Fee

Rs. 4,999/ Per Month (Affordable monthly plan)

Class Mode

Online & Offline (Interactive Live Coding)

Eligibility & Level

Beginner to Advanced (No prior coding needed)

Scholarships

Available for Offline & Deserving Students

Industry Standard Tools

Technologies & Frameworks You Will Master

Python 3.12+
NumPy
Pandas
Matplotlib
Seaborn
Streamlit
Scikit-Learn
TensorFlow & Keras
PyTorch
Generative AI & LLMs
RAG & Vector DBs
LangChain
LangGraph
AI Agents
Model Context Protocol (MCP)
WebMCP
A2A Protocol
Universal Commerce (UCP)
Git & GitHub
VS Code & Colab
Python Learning

Turn Your Ambition into an AI Career with Data Science & Machine Learning!

If you are looking for a supportive, hands-on learning environment where you learn practical coding instead of memorizing syntax, you have come to the right place. At Career Engine, we believe coding is learned by building projects.

Over 6 months, you won't just learn basic Python; you will build interactive dashboards, train machine learning models, develop computer vision classifiers, create RAG applications, and architect autonomous AI agents using industry-standard protocols.

Meet Your Instructor

Manav Singh

Manav Singh

Co-Founder & Lead Instructor

Teaches

Python, Data Science, Machine Learning, Deep Learning, Generative AI & Autonomous AI Agents

Our Proven Learning Routine

Daily 2-Hour Class Structure

Every single session is structured so no student gets left behind.

30 MIN

Theory & Concepts

Core principles, visual diagrams, real-life analogies, and problem conceptualization.

45 MIN

Live Coding

Teacher codes live in front of the class, explaining every line, error, and best practice.

35 MIN

Student Practical

Students open their laptops and code individual exercises with instant instructor guidance.

10 MIN

Doubts & Homework

1-on-1 question clearance, assigning mini challenges, and next-session preview.

FRIDAY SPECIAL
20 Mins: Weekly Concept Revision
80 Mins: Intensive Project / Practical Coding
20 Mins: Assignment Check & Doubt Solving

Career Engine Vs Others

Why Choose Career Engine?

We Are Committed to Real Industry Skills, Not Boring Theory!

⭐ CAREER ENGINE
✓
Our instructors have executed multiple domestic and overseas client software projects at www.jdwebservices.com/
✓
2 full hours of hands-on daily learning, Monday to Friday. Over 70% of total time is spent live coding.
✓
Transparent fee: You pay just Rs 4,999 per month with no hidden costs for comprehensive training.
✓
Capped class size: Maximum 15 students per batch to guarantee personal 1-on-1 attention.
✓
We help you build a professional GitHub portfolio, resume, and live LinkedIn presence that impresses hiring managers.
✓
If any class is missed due to weather, power cuts, or holidays, we deliver free extra makeup classes.
✓
Special scholarships available for offline students at our Sri Muktsar Sahib center and partner institutes.
❌ OTHER INSTITUTES & GURUS
✕
Most teachers are bookish/theoretical and have never coded real client applications or AI systems.
✕
Only 45 minutes to 1 hour of theoretical PPT slides with little or no live practical coding.
✕
Exorbitant fees (Rs 30,000 to Rs 60,000 upfront) or Rs 2,000/hr with outdated syllabi.
✕
Crowded batches of 40-60 students where you can't even get your basic doubts answered.
✕
Zero GitHub portfolio or resume guidance; students leave with just a piece of paper certificate.
✕
Missed classes are lost forever; no revision or makeup sessions are provided.
✕
No scholarships or flexible fee structures for deserving students.

Structured 6-Month Roadmap

Program Curriculum (Month-Wise)

Detailed breakdown of what you will learn and build in each month.

MONTH 1 — PYTHON FUNDAMENTALS + DEV ENVIRONMENT

MONTH 1
  1. Introduction to Programming & Python: Why Python, where it is used in Data Science, Machine Learning, Automation & AI.
  2. Installation & Setup: Python installation on Windows, PATH / Environment Variables, Python Interpreter & Terminal. Practical
  3. Visual Studio Code (VS Code): Extensions, creating & running Python files, integrated terminal, debugging basics, code formatting & interpreter selection.
  4. Jupyter Notebooks: Why Jupyter is standard in Data Science, installation, code cells vs markdown cells, notebook variables & saving.
  5. Google Colab Cloud: Colab vs VS Code, cloud execution, uploading files/CSVs, Google Drive mounting & mobile notebook execution.
  6. Git & GitHub Version Control: Git vs GitHub, repositories, committing & uploading code, professional README writing, version workflow.
  7. Python Basics & Syntax: print(), comments, variables, PEP8 naming conventions, multiple variables, clean code practices.
  8. Data Types & Casting: Integer, Float, String, Boolean, type() function, runtime user input with input(), int(), float(), str().
  9. Operators: Arithmetic (+, -, *, /, //, %, **), Assignment, Comparison (==, !=, <, >), and Logical operators (and, or, not).
  10. Conditional Logic: if, elif, else statements, nested conditions, boolean evaluation.
  11. Loops & Iterations: for loops with range(), while loops, loop controls (break, continue, pass), nested loops, star & number patterns.
  12. String Manipulation: Indexing, negative indexing, slicing [start:stop:step], methods (upper, lower, strip, replace, split, join, find, count, startswith, endswith).
  13. Modern String Formatting: f-strings, format specifiers, precision floating formatting.
  14. Hands-On Practicals & Calculators: Student Info Program, Age Calculator, Marks Calculator, Simple Billing Calculator. Practical
  15. Mini Project 1 — Student Grade Calculator: Enter marks, percentage, dynamic grade computation, pass/fail status. Mini Project
  16. Mini Project 2 — Security & Text Analyzer: Username/Password Validator, word frequency counter, text analytics tool. Mini Project

MONTH 2 — PYTHON CORE + OBJECT-ORIENTED PROGRAMMING

MONTH 2
  1. Python Lists: Creating lists, indexing, slicing, updating, append(), insert(), extend(), remove(), pop(), clear(), sorting & searching.
  2. Tuples: Immutability, tuple operations, packing & unpacking, tuple vs list performance benchmarks.
  3. Sets: Unique elements, set operations (Union, Intersection, Difference, Symmetric Difference).
  4. Dictionaries: Key-value mapping, accessing values, get(), keys(), values(), items(), nested dictionaries & dictionary comprehensions.
  5. Functions & Modularity: Creating functions with def, calling functions, parameters, arguments, return values, multiple returns.
  6. Advanced Arguments & Scope: Default arguments, keyword arguments, *args (variable positional), **kwargs (keyword arguments), LEGB variable scope.
  7. Modules & Packages: import syntax, built-in modules (math, random, datetime, sys, os), creating custom modules and packages.
  8. Package Management: pip, installing third-party packages, virtual environments (venv), generating requirements.txt, production directory layout.
  9. File Handling: File I/O (read, write, append), context managers (with open), reading line-by-line.
  10. Structured Data Persistence: Parsing & exporting CSV datasets and JSON files for application storage.
  11. Error & Exception Handling: Syntax errors vs runtime exceptions vs logical errors, try and except blocks, handling multiple exceptions, else, finally, raise.
  12. Object-Oriented Programming (OOP): Classes, objects, constructor (__init__), self instance reference.
  13. Pillars of OOP: Instance vs class attributes, methods, Inheritance, Encapsulation (private/protected), Polymorphism, and Method Overriding.
  14. Project 1 — Scientific Calculator & Student Manager: Modular console application with function dispatching and data storage. Project
  15. Month Capstone — OOP Inventory Management System: Full CRUD operations, product categories, stock tracking, search, file persistence. Capstone

MONTH 3 — NUMPY + PANDAS + DATA ANALYSIS + STREAMLIT

MONTH 3
  1. NumPy Foundations: Why NumPy is faster than Python lists, multidimensional arrays (ndarrays), array creation (zeros, ones, arange, linspace).
  2. Array Manipulation: Indexing, slicing, dimensions (ndim, shape, size), reshaping, flattening, array stacking.
  3. Vectorized Mathematics & Stats: sum(), mean(), median(), min(), max(), standard deviation, broadcasting rules, np.random simulations. Practical
  4. Pandas Foundations: Series vs DataFrames, constructing DataFrames from dicts and lists, DataFrame anatomy.
  5. Dataset Ingestion: Reading CSV, Excel (.xlsx), and JSON datasets using read_csv(), read_excel(), read_json().
  6. DataFrame Exploration: head(), tail(), info(), describe(), shape, columns, dtypes, handling memory usage.
  7. Filtering & Querying: Boolean masking, conditional filtering, selecting columns, loc[] and iloc[] indexers, sorting values.
  8. Data Cleaning (Missing Data): Detecting missing records with isnull(), imputation strategies with fillna(), removing rows with dropna().
  9. Data Cleaning (Duplicates & Types): Identifying duplicates with duplicated(), drop_duplicates(), type casting with astype().
  10. Text & DateTime Cleaning: String manipulation with .str accessors, regex replacements, parsing timestamps & extracting date features.
  11. Aggregation & Reshaping: GroupBy operations, multi-level aggregations, pivot tables, cross-tabulations.
  12. Data Visualization with Matplotlib: Line plots, bar charts, histograms, scatter plots, pie charts, box plots, styling titles & labels.
  13. Statistical Visuals with Seaborn: Correlation heatmaps, distribution plots, pairplots, categorical box & violin plots.
  14. Streamlit Web Framework: Installation, creating interactive Python web apps, inputs, sliders, file uploaders, buttons, charts & tables.
  15. Project 1 — Commercial Sales Data Analysis: Real-world revenue breakdown, top product analysis, time-series sales trends. Project
  16. Month Capstone — Interactive Streamlit Data Dashboard: Upload CSV, preview, automated cleaning, dynamic filtering, charts, statistical summaries. Capstone

MONTH 4 — EXPLORATORY DATA ANALYSIS (EDA) + MACHINE LEARNING

MONTH 4
  1. What is EDA?: Why EDA is critical in real business problems, dataset profiling, formulating analytical hypotheses.
  2. Variable Classification: Numerical variables (discrete vs continuous), Categorical variables (nominal vs ordinal), Target dependent variables.
  3. Univariate Analysis: Distribution curves, skewness, kurtosis, frequency counts, measures of central tendency & spread.
  4. Bivariate Analysis: Feature relationships, categorical vs numerical comparisons, cross-distributions, correlation scatter plots.
  5. Multivariate Analysis: High-dimensional feature interactions, multi-feature correlation matrices, complex patterns.
  6. Outlier Detection & Treatment: Interquartile Range (IQR) technique, Z-Scores, box plot identification, capping and trimming outliers.
  7. Correlation & Multicollinearity: Pearson vs Spearman correlation, correlation heatmaps, feature redundancy identification.
  8. Feature Engineering for ML: One-Hot Encoding, Label Encoding, Log transformations, preparing clean feature matrices for algorithms.
  9. Machine Learning Fundamentals: AI vs ML vs Deep Learning, Supervised vs Unsupervised learning paradigms, Features (X) & Labels (y).
  10. Data Splitting & Workflow: Training vs Testing datasets, train_test_split in Scikit-learn, random_state reproducibility.
  11. Model Lifecycle: Estimator API, model instantiation, fit(), predict(), Overfitting vs Underfitting, Bias-Variance tradeoff.
  12. Regression Algorithms: Simple Linear Regression (slope, intercept, cost function, best-fit line), Multiple Linear Regression with multiple predictors.
  13. Non-Linear Regression: Polynomial Regression, degree selection, modeling non-linear curves.
  14. Regression Evaluation Metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R² Score, Adjusted R².
  15. Major Project 1 — Complete Industry EDA Report: End-to-end data profiling, visualization, and strategic business takeaways. Major Project
  16. Major Project 2 — Real Estate House Price Predictor: Multivariate regression model with feature scaling and interactive web predictor. ML Project

MONTH 5 — CLASSIFICATION, DEEP LEARNING & GENERATIVE AI

MONTH 5
  1. Classification Algorithms: Classification principles, Logistic Regression, Sigmoid activation function, probability thresholds.
  2. K-Nearest Neighbors (KNN): Distance metrics (Euclidean, Manhattan), choosing optimal K value, decision boundaries.
  3. Tree-Based Models: Decision Trees, root nodes, splits, Gini Impurity, Entropy, Information Gain.
  4. Ensemble Learning: Random Forest Classifier, bagging, majority voting, feature importance scores.
  5. Model Evaluation Metrics: Confusion Matrix (TP, TN, FP, FN), Accuracy, Precision, Recall, F1 Score, Classification Report.
  6. Cross Validation & Pipelines: K-Fold Cross Validation, Stratified K-Fold for imbalanced datasets, StandardScaler, MinMaxScaler. Practical
  7. Unsupervised Learning (Clustering): K-Means clustering, choosing K with the Elbow Method & Silhouette score, Hierarchical clustering, Dendrograms, PCA dimensionality reduction.
  8. Deep Learning Foundations: Biological vs Artificial Neurons, Perceptron, Weights, Biases, Activation Functions (ReLU, Sigmoid, Softmax).
  9. TensorFlow & Keras: TensorFlow ecosystem, tensors, Keras Sequential API, Dense layers, Model compilation, Loss functions, Optimizers (Adam, SGD), Epochs & training.
  10. PyTorch Deep Learning: PyTorch vs TensorFlow, PyTorch Tensors, CUDA GPU acceleration, Dataset, DataLoader, torch.nn.Module, forward pass, backpropagation loop.
  11. Generative AI & LLM Foundations: What is GenAI, Large Language Models, Tokens, Context Windows, Temperature, Transformer architecture basics.
  12. AI APIs Integration: API keys, authentication, sending prompts and receiving responses from leading models (OpenAI, Gemini, Claude).
  13. Prompt Engineering: System Prompts, User Prompts, Few-Shot Prompting, Chain-of-Thought (CoT) reasoning.
  14. Structured Output & Tools: Enforcing JSON output schemas, LLM Function Calling, Native Tool Calling. GenAI
  15. Project 1 — Customer Churn & Segmentation: Dual classification and K-Means segmentation on customer behavior. Project
  16. Project 2 — PyTorch Classifier & AI Chat App: Deep learning neural network + interactive Python AI Chatbot with tool calling. Project

MONTH 6 — RAG, LANGCHAIN, LANGGRAPH, AI AGENTS & PROTOCOLS

MONTH 6
  1. Retrieval Augmented Generation (RAG): What is RAG, solving LLM knowledge cutoffs and hallucinations, domain knowledge bases.
  2. Document Processing & Embeddings: PDF & text loading, text chunking strategies (recursive splitter, chunk overlap), generating vector embeddings.
  3. Vector Databases: ChromaDB, FAISS, storing embeddings, cosine similarity search, index querying.
  4. End-to-End RAG Pipeline: User Query → Vector Search → Context Augmentation → LLM Response Synthesis with source citations.
  5. LangChain Framework: Architecture, Models, Prompt Templates, Output Parsers, LCEL Chains, Document Loaders, Vector Store retrievers.
  6. LangChain Tools & Agents: Custom tools, dynamic tool binding, Agent Executor, creating autonomous assistants.
  7. What is an AI Agent?: Chatbots vs Autonomous Agents, agent architectures, reasoning loops (ReAct pattern).
  8. LangGraph Agent Workflows: Graph-based agent execution, StateGraph, Nodes, Edges, State management, checkpointers.
  9. Multi-Step Tasks & Memory: Persistent conversation memory, tool execution, multi-step problem solving.
  10. Advanced Agent Patterns: Human-in-the-Loop approval workflows, conditional routing, multi-agent collaboration systems.
  11. Model Context Protocol (MCP) & WebMCP: MCP architecture, connecting agents to external tools/data, MCP Servers, MCP Tools, WebMCP concepts. MCP Protocol
  12. Agent-to-Agent (A2A) Protocol: Agent discovery, Agent Cards, delegation, inter-agent collaboration patterns vs MCP. A2A Protocol
  13. Universal Commerce Protocol (UCP): Autonomous commerce agents, product discovery, shopping workflows, autonomous checkout flows. UCP Protocol
  14. Project 1 — PDF Question-Answering RAG System: Ingest multi-page documents and query answers with exact page citations. RAG Project
  15. Project 2 — Autonomous AI Research Agent: LangGraph agent that searches the web, verifies data, and compiles structured reports. Agent Project
  16. Final Industry Capstone & Demonstration: Full-stack AI application combining DB, ML/RAG, Agent Tools, Protocol Integration, Streamlit UI, and live Viva presentation. Final Capstone

Showcase Your Skills

Project Portfolio

Build 15+ Industry-Grade Projects to Put on Your Resume and GitHub.

AI Research Agent

Autonomous multi-step agent with web search, real-time citation analysis, information synthesis, and automatic PDF report compilation.

Business Data Analysis Agent

Upload CSV/Excel, perform automatic data cleaning, generate interactive charts, and ask plain English questions to receive AI-driven executive insights.

AI Customer Support Agent

Enterprise customer service agent powered by vector embeddings and internal knowledge base RAG with live SQL database order lookup.

AI PDF Document Assistant

High-speed RAG application supporting multi-page PDF ingestion, semantic chunking, similarity search, and precise answers with page citations.

AI Commerce & Shopping Agent

Next-gen shopping agent implementing UCP (Universal Commerce Protocol) and A2A to search catalogs, negotiate, and execute checkout workflows.

Interactive Streamlit EDA Dashboard

Interactive web application featuring real-time data cleansing, distribution plots, Seaborn correlation heatmaps, and dynamic metric filters.

Predictive House Price ML Model

End-to-end regression model trained on multi-attribute real estate data with feature engineering, scaling, and interactive Streamlit predictor.

Customer Churn & Segmentation

Dual-model system using Random Forest classification to predict customer drop-off and K-Means clustering for customer persona segmentation.

PyTorch Deep Learning Classifier

Neural network built from scratch in PyTorch with custom DataLoader, forward pass, backpropagation, and real-time loss tracking visualization.

Tangible Deliverables

Your Portfolio After 6 Months

Every student leaves with concrete proof of practical software engineering skills.

10+
Python Mini Projects
3+
Data Analysis Projects
2+
Full EDA Reports
4+
Machine Learning Models
2+
DL & GenAI Apps
1
Industry Capstone Project

Step-by-Step Evolution

The 6-Month Learning Journey

From writing your first Python program to architecting autonomous agent networks.

Python Core
→
OOP
→
NumPy & Pandas
→
Data Cleaning & EDA
→
Machine Learning
→
Deep Learning
→
Streamlit Apps
→
Generative AI & LLMs
→
RAG & Vector DBs
→
LangGraph Agents
→
Protocols (MCP, A2A, UCP)
→
Capstone Demo

Job-Ready Capabilities

What You Will Be Able to Do

Skills that make you stand out in technical interviews and on freelance platforms.

  • Write professional, clean, modular Python programs using OOP best practices and virtual environments.
  • Clean, process, and analyze complex commercial datasets using NumPy, Pandas, and Exploratory Data Analysis.
  • Create informative, publication-grade visual charts using Matplotlib and Seaborn.
  • Train, validate, and tune Supervised (Regression, Classification) and Unsupervised (Clustering, PCA) ML models.
  • Design and build Neural Networks with TensorFlow, Keras, and PyTorch for classification tasks.
  • Deploy interactive AI and Data Science web dashboards rapidly using Streamlit.
  • Integrate cutting-edge LLM APIs using Prompt Engineering, Structured Outputs, and Tool Calling.
  • Build production-ready Retrieval Augmented Generation (RAG) systems over private PDF and document knowledge bases.
  • Develop multi-step autonomous AI agents with memory and branching workflows using LangChain & LangGraph.
  • Understand modern AI protocols: Model Context Protocol (MCP), WebMCP, A2A communication, and UCP commerce.

Ready to Start Your Python & AI Journey?

Batch sizes are strictly capped at 15 students to guarantee individual mentoring. Attend the first 5 free demo classes and see the quality yourself!

Register For Free Demo Classes Speak with Instructor on WhatsApp