Artificial Intelligence & Complete Machine Learning
Participants will understand AI concepts and build end-to-end Machine Learning models using Python and Scikit-learn.
What you'll master in Level 1
Participants will understand AI concepts and build end-to-end Machine Learning models using Python and Scikit-learn.
Training Objectives & Core Deliverables
Understand AI, ML, Deep Learning, Generative AI, and Agentic AI.
Understand the complete Machine Learning lifecycle.
Perform data preprocessing and feature engineering.
Build supervised and unsupervised ML models.
Evaluate and improve ML models.
Use Scikit-learn to create end-to-end ML workflows.
Day-by-Day Training Schedule & Live Labs
AI Foundations & ML Introduction
Identify AI use cases and explore a sample ML dataset.
Data Preparation & Feature Engineering
Clean and prepare a customer dataset for ML.
Supervised Machine Learning
Build regression and classification models using Scikit-learn.
Unsupervised Learning & Model Evaluation
Customer segmentation and model performance evaluation.
End-to-End Machine Learning Pipeline
End-to-end ML project from raw dataset to prediction.
Detailed Module-wise Curriculum
Structured vs Unstructured Data
Loading datasets using Pandas
Exploratory Data Analysis basics
Missing value treatment and duplicate removal
Outlier detection and data transformation
Feature selection and derived features
Encoding categorical variables (Label & One-Hot)
Feature Scaling (Normalization vs Standardization)
Train-Test Split
Regression problems: Simple & Multiple Linear Regression
Classification workflow: Logistic Regression
Decision Tree Classifier & Random Forest Classifier
KNN Classifier & Naive Bayes overview
Model building using Scikit-learn
Importing datasets, training models, and predicting outcomes
Unsupervised learning concepts
K-Means clustering & choosing number of clusters
Hierarchical clustering (overview)
Curse of dimensionality & PCA overview
Confusion Matrix, Accuracy, Precision, Recall, F1 Score
ROC Curve and AUC
MAE, MSE, R² Score
Cross Validation, Overfitting vs Underfitting, Bias vs Variance
Scikit-learn Pipeline: Preprocessing + Model
Hyperparameters & Grid Search tuning
Model comparison & evaluation matrices
Model Persistence: Saving models with Joblib/Pickle
ML Project Lifecycle: Dataset → Preprocessing → Training → Evaluation → Prediction
Introduction to MLOps: Model deployment overview, monitoring, retraining
Certificate of Technical Competency — Artificial Intelligence & Complete Machine Learning
Complete all live hands-on labs to earn this verifiable competency badge. This credential unlocks progression to Deep Learning with TensorFlow & Keras.
Enroll in Artificial Intelligence & Complete Machine Learning
Reserve your seat for upcoming live batches, syllabus consultation & fee structure.Frequently Asked Questions
Frequently Asked Questions: Generative AI Training Plan
Find clear answers to common questions about our live interactive classes, cohort schedules, project mentoring, and career assistance.
You can choose to enroll in the complete 4-track master program (recommended for end-to-end expertise) or consult our academic team to join specific tracks matching your current skills.
Track 1: Scikit-learn, MLOps, ML Pipelines. Track 2: TensorFlow, Keras, CNN, NLP. Track 3: LangChain, Chroma/FAISS, RAG, Fine-Tuning. Track 4: LangGraph, MCP, ReAct, Multi-Agent Systems.
Yes, all classes are live, interactive online sessions with senior practitioners. Session recordings, code notebooks, datasets, and reading references are shared after each class.
Ready to Start Your Tech Journey?
Connect with our academic training counsellors. We analyze your background, career goals, and recommend the best live online track for your success.