CODEBHAVYA • ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Learn AI. Understand the Algorithms. Build Intelligent Systems.
A complete concept-to-career path that turns mathematics, data and algorithms into working AI systems. Learn visually, trace every important computation, implement models in Python, build deployable projects and prepare for AI & ML interviews with confidence.
✓ Visual intuition before formulas
✓ Algorithms implemented from scratch
✓ Placement checkpoints in every level
26 Structured Levels
60+ Core Algorithms
30+ Visual Labs
Career Integrated Track
CODEBHAVYA AI LAB LIVE LEARNING PATH
DATA LEARN REPRESENT PREDICT
model.fit(data)
CODEBHAVYA MOTTO From Learning to Limitless Possibilities.
This course is designed to take a learner from first principles to portfolio-ready, placement-ready AI engineering.
BEFORE YOU BEGIN
Build the Right Foundation—No Blind Library Calls
Basic Python is recommended. Every required mathematical idea is rebuilt inside this course with intuition, calculation, visualization and code, so motivated beginners can follow the complete path in order.
🐍 Python basics ➗ School-level algebra 📊 Curiosity about data 🧠 Willingness to practise
COMPLETE COURSE STRUCTURE
Five-Part AI & ML Learning Path
Progress from foundations to modern AI systems without skipping the reasoning that makes algorithms understandable.
PART 1 • LEVELS 1–5 01 Foundations for AI Python, mathematics, probability, data analysis and preparation.
5 levels • Beginner PART 2 • LEVELS 6–12 02 Core Machine Learning Regression, classification, trees, SVM, ensembles and evaluation.
7 levels • Core PART 3 • LEVELS 13–16 03 Unsupervised & Applied ML Clustering, representation, anomalies and recommendations.
4 levels • Applied PART 4 • LEVELS 17–22 04 Deep & Generative AI Neural networks, vision, NLP, LLMs, RAG and agents.
6 levels • Advanced PART 5 • LEVELS 23–26 05 Intelligent Systems & Career Classical AI, reinforcement learning, MLOps and placement.
4 levels • Career
PART 1 • LEVELS 1–5 Foundations for AI Understand the complete learning workflow and build the coding, mathematical and data foundations every model requires.
FOUNDATION
LEVEL 01 🧭
AI & ML Foundations Separate AI, ML and deep learning; understand datasets, features, labels, models, training and inference.
AI problem types and lifecycle Supervised, unsupervised and reinforcement learning Training, validation and testing First end-to-end prediction Visualizer Workflow Tracer Placement Basics
LEVEL 02 🐍
Python, NumPy & Data Tools Use the Python data stack efficiently and connect arrays, tables and plots with model-ready workflows.
NumPy arrays and vectorization Pandas selection, grouping and joins Matplotlib and Seaborn Notebook-to-script workflow Code Lab Array Visualizer Interview Tasks
LEVEL 03 📐
Linear Algebra for Machine Learning See how vectors, matrices and transformations represent data and power model computations.
Vectors, matrices and tensors Dot products and matrix multiplication Rank, inverse and projections Eigenvalues, eigenvectors and SVD Matrix Visualizer Step Tracer Math Interview
LEVEL 04 🎲
Probability & Statistics Reason about uncertainty, distributions, sampling and statistical evidence used throughout machine learning.
Conditional probability and Bayes rule Random variables and distributions Expectation, variance and covariance Hypothesis tests and confidence intervals Distribution Lab Bayes Tracer Aptitude
LEVEL 05 🧹
Data Preparation, EDA & Features Turn imperfect raw data into trustworthy model inputs while preventing leakage and misleading conclusions.
Missing values and outliers Scaling and categorical encoding Feature engineering and selection EDA, imbalance and data leakage Data Pipeline EDA Lab Case Study
PART 2 • LEVELS 6–12 Core Machine Learning Learn the essential supervised algorithms from intuition and mathematics to implementation, diagnostics and selection.
CORE ML
LEVEL 06 📈
Regression & Gradient Descent Predict continuous values and understand how optimization learns the best-fitting parameters.
Simple and multiple linear regression Loss, gradients and normal equation Polynomial regression Ridge, Lasso and Elastic Net Fit-Line Visualizer Gradient Tracer From Scratch
LEVEL 07 🎯
Logistic Regression & Classification Estimate class probabilities, choose decision thresholds and evaluate classification responsibly.
Log-odds, sigmoid and cross-entropy Binary and multiclass classification Confusion matrix and thresholding Precision, recall, F1, ROC and PR-AUC Boundary Visualizer Metric Explorer Interview Focus
LEVEL 08 🧭
k-NN & Naive Bayes Compare instance-based and probabilistic learning through distance, neighbourhoods and Bayes reasoning.
Distance metrics and choosing k Curse of dimensionality Gaussian, Multinomial and Bernoulli NB Probability smoothing Neighbourhood Lab Bayes Tracer Comparison
LEVEL 09 🌳
Decision Trees & Random Forests Build interpretable decision rules and strengthen them through randomized ensemble learning.
Entropy, Gini and information gain Tree growth, pruning and overfitting Bagging and random subspaces Feature importance and interpretation Tree Builder Split Tracer Placement Problems
LEVEL 10 🛡️
Support Vector Machines & Kernels Learn maximum-margin classification and use kernels to separate non-linear data.
Hyperplanes and support vectors Hard margin, soft margin and C Kernel trick Linear, polynomial and RBF kernels Margin Visualizer Kernel Explorer Math Trace
LEVEL 11 ⚡
Ensemble Learning Combine weak and diverse models to improve stability, accuracy and generalization.
Voting, bagging and stacking AdaBoost weight updates Gradient boosting XGBoost concepts and tuning Boosting Visualizer Weight Tracer Model Battle
LEVEL 12 🧪
Evaluation, Validation & Tuning Design reliable experiments and select models using evidence instead of a single attractive score.
Classification and regression metrics Thresholds, calibration and error analysis Cross-validation and leakage-safe pipelines Grid, random, halving and nested selection Metric Laboratory CV Search Engine Selection Tracer
PART 3 • LEVELS 13–16 Unsupervised & Applied Machine Learning Discover structure without labels and build models for anomalies, similarity and personalization.
APPLIED ML
LEVEL 13 🫧
Clustering Algorithms Group unlabeled data using centroids, density, hierarchy and probabilistic membership.
K-Means and K-Means++ Hierarchical clustering DBSCAN Gaussian Mixture Models and silhouette score K-Means Laboratory DBSCAN Engine True Loop Tracer
LEVEL 14 🗜️
Dimensionality Reduction Compress features, remove redundancy and reveal useful low-dimensional structure.
PCA and explained variance LDA for supervised projection SVD and latent structure t-SNE and UMAP visualization PCA Geometry Lab SVD Compression Lab True Loop Tracer
LEVEL 15 🚨
Anomaly Detection Identify rare, suspicious or faulty patterns in finance, networks, machines and health data.
Statistical outlier rules Isolation Forest Local Outlier Factor and One-Class SVM Autoencoder-based anomalies Detection Workbench Isolation Path Lab True Loop Tracer
LEVEL 16 🎬
Recommendation Systems Retrieve, score and rank useful items using content, collaborative evidence, latent factors and responsible product decisions.
Popularity, content and collaborative filtering Matrix factorization and implicit ranking Precision@K, Recall@K, NDCG and coverage Cold start, diversity and production design Ranking Workbench Factor SGD Lab True Loop Tracer
PART 4 • LEVELS 17–22 Deep Learning & Generative AI Move from neurons and backpropagation to vision, language, transformers and grounded generative applications.
MODERN AI
LEVEL 17 🧠
Neural Networks & Backpropagation Build multilayer networks and trace how gradients update every weight.
Perceptron and multilayer networks Activations and initialization Forward and backward propagation SGD, momentum, RMSProp and Adam Neuron Laboratory XOR Network Trainer Backprop Tracer
LEVEL 18 👁️
Computer Vision & CNNs Learn spatial feature extraction and build image classification, detection and segmentation pipelines.
Convolution, filters and pooling CNN architectures and receptive fields Transfer learning and augmentation Detection and segmentation concepts Kernel Workbench CNN Shape Laboratory Convolution Tracer
LEVEL 19 📡
Sequences & Time-Series Learning Model ordered observations using statistical forecasting and recurrent neural networks.
Trends, seasonality and stationarity Baselines, ARIMA and forecasting metrics RNN, LSTM and GRU Sequence attention and windowing Forecast Workbench Memory Laboratory True Loop Tracer
LEVEL 20 💬
Natural Language Processing Transform language into useful representations and solve classification, sequence and extraction tasks.
Text cleaning, tokenization and n-grams Bag of Words and TF-IDF Word2Vec and embeddings Sentiment, NER and sequence labelling Token Vector Workbench Semantic Geometry Lab Naive Bayes Tracer
LEVEL 21 ✨
Transformers & Large Language Models Trace self-attention and understand how modern foundation models represent, train and generate text.
Scaled dot-product and multi-head attention Positional encoding and masking Encoder, decoder, BERT and GPT families Fine-tuning, PEFT and evaluation Attention Matrix Lab Generation Workbench True Loop Tracer
LEVEL 22 🪄
Generative AI, RAG & Agents Build grounded AI applications and understand the generative methods behind modern text and image systems.
Autoencoders, VAEs, GANs and diffusion Prompting and structured outputs Embeddings, vector search and RAG Tools, memory, agents and evaluation RAG Workbench Agent Laboratory Hybrid Retrieval Tracer
PART 5 • LEVELS 23–26 Intelligent Systems, Production & Career Complete the AI picture with search, decision-making, reliable deployment, responsible practice and placement mastery.
CAREER READY
LEVEL 23 ♟️
Classical AI, Search & Reasoning Solve goal-driven problems using search, adversarial reasoning, constraints and knowledge.
Uniform-cost, greedy and A* search Minimax and alpha–beta pruning Constraint satisfaction Knowledge representation and Bayesian networks A* Search Laboratory Alpha–Beta Laboratory True Loop Tracer
LEVEL 24 🎮
Reinforcement Learning Learn how agents improve decisions through rewards, value estimates and exploration.
MDPs and Bellman equations Dynamic programming and Monte Carlo Temporal-difference and Q-learning Deep Q-Network concepts Bellman Gridworld Lab Q-Learning Training Lab True Loop Tracer
LEVEL 25 🚀
MLOps, Deployment & Responsible AI Move models from notebooks to monitored services while managing reproducibility, drift, fairness and risk.
Experiment tracking and model registry FastAPI, containers and deployment Monitoring, drift and retraining Fairness, privacy, robustness and governance Deployment Pipeline Lab Drift & Fairness Monitor True Loop Tracer
LEVEL 26 🏆
Capstone Projects & Placement Mastery Convert knowledge into proof through projects, interviews, coding rounds and end-to-end system discussions.
Guided mini-projects and capstones Python, SQL and ML coding rounds ML theory and case interviews Resume, GitHub and project defence Mock Interview Project Review Placement Arena
LEVEL 27 🧩
AI & ML Case Studies Apply knowledge into proof through case-studies to Frame, train, evaluate and responsibly defend two complete classification projects.
Student Placement Outcome Modeling Responsible Medical Classification Audit Baselines, leakage checks and error analysis Case Review Programs Online Compiler
ALGORITHM ATLAS
Know What to Use, Why It Works and When It Fails
Choose a family to preview the algorithm-selection thinking reinforced throughout the course.
Supervised
Unsupervised
Deep Learning
Language & GenAI
Search & RL
SUPERVISED LEARNING
Learn from labelled examples
Predict continuous values or known classes, then measure how well the model generalizes to unseen data.
Linear Regression Logistic Regression k-NN Naive Bayes Decision Trees Random Forest SVM Boosting
Selection question Is the target continuous or categorical, and what trade-off is required between accuracy, speed and interpretability?
Training Canvas Initial model
Study Hours Predicted Score
Live Training State Ready
STEP 0 / 8
SLOPE (w) 0.00
BIAS (b) 10.00
MEAN SQUARED ERROR 2428.00
WHAT IS HAPPENING? The model begins with a poor line. Gradient descent will measure the error and update the slope and bias.
Predict → Measure Loss → Compute Gradient → Update
← Previous
Next Step →
▶ Auto Run
Ⅱ Pause
↻ Reset
Step 0 of 8
BUILD AS YOU LEARN A Four-Stage Project Ladder Every stage converts concepts into evidence that can be demonstrated in a portfolio and defended in an interview.
STAGE 01 • DATA 📊 Insight Project Clean a real dataset, perform EDA, engineer useful features and communicate trustworthy findings.
Data quality report Visual story Reproducible notebook STAGE 02 • MACHINE LEARNING 🧪 Predictive System Compare multiple algorithms, validate correctly and explain the selected model’s behaviour.
Baseline and experiments Metric justification Error analysis STAGE 03 • DEEP LEARNING 🧠 Vision or Language Model Train a neural solution, track learning, prevent overfitting and evaluate difficult examples.
PyTorch training pipeline Ablation comparison Model interpretation STAGE 04 • PRODUCTION AI 🚀 Deployed AI Application Ship an ML or grounded GenAI application with an API, evaluation, monitoring and documentation.
Service and interface Experiment lineage Placement-ready case study
PLACEMENT-FIRST PREPARATION Prepare for Every AI & ML Hiring Round Placement preparation is integrated throughout the course and completed with a dedicated final mastery level.
01 🐍 Python & SQL Coding Data manipulation, functions, NumPy, Pandas, SQL queries and timed coding tasks.
02 📐 Math & Statistics Vectors, probability, distributions, estimation and numerical interview problems.
03 🧠 ML Theory Algorithm assumptions, loss functions, metrics, trade-offs and failure analysis.
04 🧪 Case Studies Frame ambiguous business problems, choose data, metrics and validation strategies.
05 🏗️ ML System Design Training pipelines, serving, scale, latency, monitoring, drift and retraining.
06 🏆 Portfolio & Project Defence Build credible projects and explain every technical and product decision clearly.
THE CODEBHAVYA LEARNING STANDARD Every Level Will Follow One Premium Pattern Consistent structure helps learners understand, practise, recall and communicate each concept.
01 Quick Revision Recall prerequisites and essential definitions.
02 Visual Intuition See the problem before reading formulas.
03 Math & Algorithm Derive the logic and trace every step.
04 Python Implementation Code from scratch, then use libraries correctly.
05 Premium Visualizer & Tracer Control execution and observe live state.
06 Practice & Placement Solve problems, cases and interview questions.
07 Project Connection Apply the concept in a realistic system.
08 Key Takeaway Finish with durable rules and next steps.
YOUR AI JOURNEY STARTS HERE Do Not Memorize Models. Learn to Think Like an AI Engineer. Begin with Level 1, follow the roadmap in order and turn every concept into visible understanding, working code and placement-ready evidence.
Start with AI & ML Foundations →
No matching AI & ML topic found. Try an algorithm name such as “SVM”, “PCA”, “CNN”, “RAG” or “Q-learning”.
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