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AI Integrated
Machine Learning Course

Go from statistics & algorithms to deployed models in 3 months. Master the fundamental algorithms and statistical models that power modern artificial intelligence, and build real predictive models with scikit-learn and Python.

Start Learning Watch Demo

train_model.py

1 from sklearn.model_selection import train_test_split

2 from sklearn.ensemble import RandomForestClassifier

4 data = pd.read_csv("dataset.csv")

5 X = data.drop("target", axis=1)

6 y = data["target"]

8 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

10 model = RandomForestClassifier(n_estimators=100)

11 model.fit(X_train, y_train)

12 print(f"Accuracy: {model.score(X_test, y_test)*100:.1f}%")

What You'll Master

Three Core Skill Tracks

Structured learning paths designed to take you from statistics fundamentals to deployed, production-ready ML models.

Statistics & ML Foundations

Build the statistical intuition every ML practitioner needs before touching a model.

Probability & statistics for ML
Data preprocessing & feature engineering
Exploratory data analysis
Working with pandas & NumPy

Core Skill

Core ML Algorithms & Model Building

Learn and apply the algorithms that power real-world predictive systems with scikit-learn.

Supervised learning (regression & classification)
Unsupervised learning (clustering)
Model evaluation & tuning with scikit-learn
Cross-validation & performance metrics

Real-World Projects & Deployment

Apply everything to messy, real datasets and ship models people can actually use.

Working with real-world datasets
Model deployment basics
Intro to neural networks
Bridge into deep learning

See It In Action

Build Real ML Models From Day One

No boring theory. You'll train, evaluate, and deploy practical models that solve real problems. Each project is designed to teach you the machine learning skills employers actually want.

House Price Prediction Model

Build a regression model that predicts real estate prices from property features.

Customer Churn Classifier

Train a classification model to predict which customers are likely to churn.

Image Classification with CNN Basics

Get hands-on with convolutional neural network fundamentals for image data.

Sentiment Analysis Pipeline

Build an end-to-end pipeline that classifies text sentiment from real data.

View Full Curriculum

churn_model.py

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

def train_churn_model(df):
    X = df.drop("churned", axis=1)
    y = df["churned"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    model = LogisticRegression()
    model.fit(X_train, y_train)
    return model, X_test, y_test

df = pd.read_csv("customers.csv")
model, X_test, y_test = train_churn_model(df)

score = model.score(X_test, y_test)
print(f"Model Accuracy: {score*100:.1f}%")

# Deploy instantly as a REST API
print("Model deployed successfully! 🚀")

Complete Syllabus

Machine Learning & Statistical Modeling

3 Month Program • Beginner to Intermediate • 4 Comprehensive Modules

Statistics & Data Foundations

Build the statistical and data-handling foundation every ML model depends on

Probability & Statistics Data Preprocessing Feature Engineering Exploratory Data Analysis

Apply core statistical concepts to real datasets Clean and preprocess raw data for modeling Perform exploratory data analysis with pandas

Supervised Learning Algorithms

Learn to build and train models that predict outcomes from labeled data

Regression Models Classification Models Scikit-learn Decision Trees

Build regression models to predict continuous values Build classification models for categorical outcomes Train and evaluate models using scikit-learn

Unsupervised Learning & Model Evaluation

Uncover hidden patterns in data and learn to rigorously evaluate model performance

Clustering Dimensionality Reduction Model Tuning Evaluation Metrics

Apply clustering algorithms to uncover patterns in data Tune hyperparameters for optimal model performance Evaluate models using precision, recall & accuracy

Real Projects & Deployment

Apply your skills to real datasets and ship your first deployed ML models

Real Datasets Model Deployment API Integration Neural Network Basics

Deploy trained models as usable applications Work confidently with real-world, messy datasets Understand neural networks as a foundation for deep learning

Expert Guidance

Learn from Industry Experts

Get mentored by professionals who build real-world machine learning and AI systems every day.

5+ Years
Experience

Saravana Perumal

Python & GenAI Mentor

A seasoned practitioner specializing in statistical modeling, machine learning engineering, and translating algorithms into production systems. Saravana has built and deployed predictive models across regression, classification, and clustering problems for real business use cases. His mentorship focuses on turning statistical theory into models you can actually ship, monitor, and maintain.

Statistical Modeling ML Engineering Data Science

Do I need a math background for this course?

You don't need an advanced math degree. We build up the probability, statistics, and linear algebra concepts you need from the ground up, alongside the ML algorithms themselves, so you understand the "why" behind every model.

What will I be able to build after this course?

You'll be able to build regression and classification models, run clustering analysis, evaluate and tune models with scikit-learn, and deploy trained models as working applications — including a basic introduction to neural networks.

Will I get a certificate?

Yes! You'll receive an industry-recognized Machine Learning Certificate from BlackBox Learning with a unique verification ID. It's LinkedIn-ready and employer-accepted.

What makes this course different?

Most courses cover only theory. We focus 100% on practical, project-based learning. You'll build real-world ML projects, work with real datasets and industry tools, and get live mentorship throughout the program.

What careers can I pursue after this?

ML Engineer, Data Scientist, AI Analyst, Machine Learning Developer, Data Analyst — the demand for machine learning professionals has never been higher.

Online or offline?

Blended format — live in-person sessions at our Thiruvananthapuram center plus online access to recordings, resources, and mentor support. Best of both worlds.

AI Integrated
Machine Learning Course

Three Core Skill Tracks

Statistics & ML Foundations

Core ML Algorithms & Model Building

Real-World Projects & Deployment

Build Real ML Models From Day One

House Price Prediction Model

Customer Churn Classifier

Image Classification with CNN Basics

Sentiment Analysis Pipeline

Machine Learning & Statistical Modeling

Statistics & Data Foundations

Supervised Learning Algorithms

Unsupervised Learning & Model Evaluation

Real Projects & Deployment

Learn from Industry Experts

Saravana Perumal

Ready to Start Your Machine Learning Journey?

Got Questions?

Book Your Free Demo Class

AI Integrated Machine Learning Course

Three Core Skill Tracks

Statistics & ML Foundations

Core ML Algorithms & Model Building

Real-World Projects & Deployment

Build Real ML Models From Day One

House Price Prediction Model

Customer Churn Classifier

Image Classification with CNN Basics

Sentiment Analysis Pipeline

Machine Learning & Statistical Modeling

Statistics & Data Foundations

Supervised Learning Algorithms

Unsupervised Learning & Model Evaluation

Real Projects & Deployment

Learn from Industry Experts

Saravana Perumal

Ready to Start Your Machine Learning Journey?

Got Questions?

Book Your Free Demo Class

AI Integrated
Machine Learning Course