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Python With Gen Ai
Beginner
Python With Gen Ai
Expert Trainer
Expert Instructor
★★★★★ (25)
Master the power of Python with Generative AI and build real-world intelligent applications. This course covers everything from Python fundamentals to advanced AI concepts, enabling you to create smart solutions using modern tools and technologies. Gain hands-on experience through practical projects and become industry-ready in the fast-growing field of AI. Data Handling & Basic Visualization
What You'll Learn
Python Fundamentals
Object-Oriented Programming (OOP) Concepts
Working with Files & APIs
Prompt Engineering Advanced
Data Handling & Basic Visualization
Course Curriculum
MONTH-1 Python Core Mastery
Week 1 Python Fundamentals
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Variables, data types, operators
Strings & string methods
Input/output, type casting
f-strings, format()
Week 2 Control Flow & Functions
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if/elif/else, loops (for, while)
List, tuple, dict, set
Functions, *args, **kwargs
Lambda, map, filter, zip
Week 3 OOP — Object Oriented Python
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Classes, objects, constructors
Inheritance, polymorphism
Dunder methods (__str__, __repr__)
Decorators (@property, @staticmethod)
Week 4 File Handling & Modules
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File read/write, context managers
JSON, CSV handling
os, sys, pathlib modules
Virtual env, pip, requirements.txt
MONTH-2 DSA
Week 5 Advanced Data Structures
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Stack, Queue, Deque
Linked List implementation
Hash Maps, Sets internals
Heaps & Priority Queue
Week 6 Searching & Sorting
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Binary search & variants
Merge sort, Quick sort
Time & Space complexity (Big O)
Two pointers, Sliding window
Week 7 Recursion & Dynamic Programming
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Recursion + memoization
Fibonacci, factorial patterns
1D DP (house robber, coin change)
Leetcode Easy–Medium practice
Week 8 Graphs & Trees
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BFS, DFS traversal
Binary trees, BST
Graph representation (adj list)
Leetcode pattern practice
MONTH-3 PYTHON FOR DATA SCIENCE & DATABASES
Week-9 NumPy & Pandas
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NumPy arrays, indexing, broadcasting, vectorized operations
Pandas Series & DataFrame, import/export (CSV, Excel, JSON)
Filtering, sorting, groupby, merge/join operations
Handling missing data and duplicates
Week-10: Data Visualization (Tools: Matplotlib, Seaborn, Plotly)
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Line, bar, scatter, histogram, box plots
Seaborn statistical plots — heatmap, pairplot, violin plot
Interactive dashboards with Plotly
Storytelling with data — choosing the right chart
Week 11: SQL for Data Science (Tools: MySQL / PostgreSQL)
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SQL basics — SELECT, WHERE, ORDER BY, GROUP BY
Joins (INNER, LEFT, RIGHT, FULL), subqueries
Aggregate functions, window functions
Connecting Python to SQL databases
Week 12: Git, GitHub, APIs & Web Scraping
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Git basics — commit, branch, merge, pull request
Building a professional GitHub portfolio
REST APIs — requests module, JSON parsing
Web scraping fundamentals with BeautifulSoup
MONTH-4 — STATISTICS & EXPLORATORY DATA ANALYSIS
Week 13: Statistics & Probability for ML
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Descriptive statistics — mean, median, mode, variance, std dev
Probability fundamentals, distributions (Normal, Binomial)
Correlation vs Causation
Hypothesis testing basics, p-values
Week 14: Linear Algebra & Calculus Essentials
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Vectors, matrices, matrix operations (via NumPy)
Dot product, eigenvalues/eigenvectors — intuition
Derivatives and gradients — intuition for ML
Why these concepts matter for ML/DL (analogy-based)
Week 15: Exploratory Data Analysis (EDA)
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Univariate, bivariate, multivariate analysis
Outlier detection and treatment
Feature engineering basics — encoding, scaling
Real Kaggle dataset — full EDA case study
Week 16: Data Preprocessing Pipeline
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Handling categorical & numerical data
Train-test split, data leakage awareness
Building reusable preprocessing pipelines (Scikit-learn)
Preparing a dataset end-to-end for modeling
MONTH -5 — MACHINE LEARNING
Week 17: Supervised Learning — Regression (Tools: Scikit-learn)
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Linear Regression (theory + from-scratch simulation)
Multiple & Polynomial Regression
Regularization — Ridge, Lasso
Evaluation metrics — MAE, MSE, RMSE, R²
Week 18: Supervised Learning — Classification (Tools: Scikit-learn)
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Logistic Regression, Decision Trees, KNN
Naive Bayes, Support Vector Machines (SVM)
Confusion Matrix, Precision, Recall, F1-score, ROC-AUC
Handling imbalanced datasets
Week 19: Model Evaluation & Tuning
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Cross-validation techniques (K-Fold)
Hyperparameter tuning — GridSearchCV, RandomizedSearchCV
Bias-Variance tradeoff, overfitting/underfitting
Model selection strategy
Week 20: Ensemble Learning & Unsupervised Learning
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Random Forest, Gradient Boosting, XGBoost
K-Means Clustering, Hierarchical Clustering
Dimensionality Reduction — PCA
Capstone ML mini-project (classification/regression)
MONTH-6 — DEEP LEARNING
Week 21: Neural Network Foundations
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Biological vs artificial neuron, Perceptron
Weights, bias, activation functions (Sigmoid, ReLU, Softmax)
Forward propagation — pure Python simulation
Loss functions and Gradient Descent intuition
Week 22: Training Deep Networks (Tools: TensorFlow/Keras)
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Backpropagation concept (analogy-based, no heavy math)
Building your first ANN with Keras
Overfitting solutions — Dropout, Regularization, Early Stopping
Model saving, loading & inference
Week 23: Convolutional Neural Networks (CNN)
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Why CNNs for images — filters, kernels, pooling
Building an image classifier with Keras
Transfer Learning (using pre-trained models)
Real-world computer vision use cases
Week 24: Recurrent Neural Networks (RNN/LSTM)
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Why RNNs for sequential/text data
LSTM & GRU — intuition for handling memory
Text preprocessing for deep learning
Deep Learning capstone project (image or text classifier)
MONTH-7 — GENERATIVE AI & LARGE LANGUAGE MODELS
Week 25: Generative AI Foundations
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Generative vs Discriminative AI
Evolution: RNN → Transformer → LLM
Tokens & Tokenization — pure Python simulation
Embeddings — converting text into meaningful vectors
Week 26: Transformers, LLMs & Prompt Engineering
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Attention mechanism — analogy-based explanation
Context window, next-token prediction (simulation)
Prompt Engineering — zero-shot, few-shot, chain-of-thought
Working with LLM APIs (OpenAI, Anthropic, Gemini)
Week 27: LangChain & Vector Databases (Tools: LangChain, ChromaDB/FAISS)
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LangChain fundamentals — chains, prompts, memory
Vector databases and semantic search
Document loaders, text splitting strategies
Building a simple LLM-powered chatbot
Week 28: RAG Systems & AI Agents
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Retrieval-Augmented Generation (RAG) — architecture
Building a RAG-based Q&A system over custom documents
Intro to AI Agents and tool-calling
Responsible AI — limitations, hallucination, ethics
MONTH-8 — DEPLOYMENT, MLOPS & PLACEMENT PREPARATION
Week 29: Model & App Deployment (Tools: Flask, FastAPI, Streamlit, Docker)
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Building APIs for ML/GenAI models (Flask/FastAPI)
Building interactive demos with Streamlit
Docker basics — containerizing an application
Deploying to cloud platforms (Render/AWS/Azure basics)
Week 30: MLOps & Best Practices (Tools: GitHub Actions, AWS/Azure basics)
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MLOps overview — model lifecycle management
Version control for models & datasets
CI/CD pipeline basics
Monitoring and logging fundamentals
Week 31: Capstone Project
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End-to-end AI/GenAI application (student's own choice)
Combines ML/DL + GenAI + Deployment
Code review, documentation, and GitHub README polishing
Project presentation to instructors || peers
Week 32: Placement Preparation
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Resume building tailored for AI/ML/GenAI roles
LinkedIn & GitHub portfolio optimization
Technical interview prep — DSA + ML/GenAI concept rounds
Mock interviews, HR round preparation & soft skills
Requirement For This Course
Computer / Mobile
Internet Connection
Paper / Pencil
Course Includes
250 Lessons
500
Beginner
Hindi/English
25+ Enrolled
Certificate on Completion
FAQ
Course FAQs
Is course ke baare mein common questions.
Are the training programs suitable for beginners as well as advanced learners?
