AI & Machine Learning Program

AI & Machine Learning is a live, instructor-led program designed to build strong skills in artificial intelligence, machine learning algorithms, and intelligent system development using modern AI frameworks and tools.

  • This 24-week structured journey includes real AI and machine learning projects, 10+ hands-on workshops, 30+ real-world AI use cases, and masterclasses with continuous mentorship.
  • You will work with industry tools such as Python, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, Jupyter Notebook, and deep learning frameworks.
  • The program also provides an option to pursue industry-recognized certifications including AWS Machine Learning Specialty, Microsoft Azure AI Engineer Associate, Google Professional Machine Learning Engineer, and IBM AI Engineering Professional Certificate.
Format Live Instructor-Led
Duration 24 Weeks | 180 Hours
Prerequisites Data Science program or experience with Python, data analysis, and basic statistics
Admission Deadline 30 April 2026
Case Studies & Projects 30+
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Key Program Takeaways

Build real AI and machine learning capability through guided model development labs, algorithm workflows, and project-based learning designed for intelligent systems and predictive applications.

Machine Learning Foundations

Supervised & Unsupervised Learning, Model Training

Deep Learning

Neural Networks, TensorFlow, PyTorch

Data Processing

Python, Pandas, NumPy, Feature Engineering

Model Evaluation

Model Testing, Performance Metrics, Optimization

AI Applications

Computer Vision, NLP Basics, Intelligent Systems

Capstone Portfolio

End-to-End AI/ML Project with Real-World Datasets

List of Modules in this Program

01

AI & Machine Learning Foundations

  • Introduction to artificial intelligence and machine learning concepts.
  • Understand the AI/ML lifecycle from data preparation to model deployment.
  • Work with datasets using Python and Jupyter Notebook.
02

Python for AI & Machine Learning

  • Learn Python programming for machine learning workflows.
  • Work with libraries such as Pandas, NumPy, and Matplotlib.
  • Perform data preprocessing, feature engineering, and transformation.
03

Machine Learning Algorithms

  • Understand supervised and unsupervised learning techniques.
  • Implement regression, classification, clustering, and decision tree algorithms.
  • Train and evaluate models using Scikit-learn.
04

Deep Learning

  • Introduction to neural networks and deep learning architectures.
  • Build models using TensorFlow and PyTorch.
  • Apply deep learning techniques for image and text data.
05

AI Applications

  • Explore computer vision, NLP, and recommendation systems.
  • Learn deployment basics and scalable AI workflows.
  • Build AI-powered solutions for real-world problems.
06

Capstone Project

  • Build and train machine learning models using real-world datasets.
  • Apply the full AI workflow from data preparation to model evaluation.
  • Present a portfolio-ready AI/ML project with predictive insights.

Hands-On Roadmap

Weeks 1–4

AI Foundations & Environment Setup

  • Install Python, Jupyter Notebook, and AI development tools
  • Work with datasets and perform data preprocessing
  • Learn feature engineering and dataset preparation
  • Hands-on: Dataset Profiling Engine, Data Cleaning Automation Tool
Weeks 5–8

Machine Learning with Python

  • Use Pandas, NumPy, and Scikit-learn for modeling workflows
  • Implement regression and classification algorithms
  • Perform model training and evaluation
  • Hands-on: Startup Success Predictor, Student Performance Prediction Model
Weeks 9–12

Advanced Machine Learning

  • Decision Trees, Random Forest, and Gradient Boosting
  • Model evaluation techniques and hyperparameter tuning
  • Feature importance and model interpretation
  • Hands-on: Customer Lifetime Value Predictor, Dynamic Pricing Model
Weeks 13–16

Deep Learning

  • Build neural networks and deep learning architectures
  • Build models using TensorFlow and PyTorch
  • Train models on structured and image datasets
  • Hands-on: Handwritten Digit Recognition System, Image Quality Classifier
Weeks 17–20

AI Applications

  • Natural Language Processing fundamentals
  • Recommendation engines and intelligent systems
  • Build scalable AI pipelines
  • Hands-on: News Topic Classifier, Resume Skill Extraction System
Weeks 21–24

Capstone AI Project

Develop a complete AI solution using real-world datasets.
  • Design end-to-end AI architecture
  • Train and evaluate machine learning or deep learning models
  • Deploy models and present insights
  • Capstone project options:
  • AI-Powered Career Recommendation Platform
  • Smart Document Intelligence System
  • Demand Forecasting AI for Retail Products

Top Companies Hiring AI & Machine Learning Engineers

Leading technology companies, AI labs, consulting firms, and digital platforms actively hire AI and machine learning engineers to build intelligent systems and automation platforms.

Amazon Google Microsoft Meta Apple IBM NVIDIA OpenAI McKinsey & Company BCG Bain & Company Deloitte Accenture Goldman Sachs JPMorgan Chase Morgan Stanley Flipkart Paytm Razorpay Swiggy Zomato PhonePe Infosys TCS Wipro Cognizant

Some of our exceptional outcomes with top companies.

Sai Arya

Sai Arya

₹65 LPA CTC

Nitish

Nitish

₹65 LPA CTC

Manurbhav A

Manurbhav A

₹21 LPA CTC

Aditya T

Aditya T

₹21 LPA CTC

Om Nakrani

Om Nakrani

₹65 LPA CTC

Yuvana T

Yuvana T

₹22 LPA CTC

Pardhiv A

Pardhiv A

₹21 LPA CTC

Phanindra B

Phanindra B

₹19 LPA CTC

Sai Gokul

Sai Gokul

₹65 LPA CTC

Abhigyan N

Abhigyan N

₹65 LPA CTC

Aryan T

Aryan T

₹21 LPA CTC

Devendra A

Devendra A

₹65 LPA CTC

Mahesh Reddy

Mahesh Reddy

₹65 LPA CTC

Prakul K

Prakul K

₹21 LPA CTC

Prashanth Reddy K

Prashanth K

₹65 LPA CTC

Rajeev T

Rajeev T

₹21 LPA CTC

Master Technologies

Core AI, machine learning, deep learning, and data engineering technologies used throughout the program.

Python logoPython
Pandas logoPandas
NumPy logoNumPy
Jupyter logoJupyter Notebook
Scikit-learn logoScikit-learn
TensorFlow logoTensorFlow
PyTorch logoPyTorch
Keras logoKeras
OpenCV logoOpenCV
Hugging Face logoHugging Face Transformers
SQL logoSQL
Apache Spark logoApache Spark
AWS AI & Machine Learning
Azure AI
Google Cloud AI logoGoogle Cloud AI
MLflow logoMLflow

Eligibility & Admission

A fully online, straightforward admissions process with advisor support throughout enrollment.

Who Can Apply Eligibility requirements for enrollment
  • Graduates in computer science, IT, data science, mathematics, engineering, or related disciplines.
  • Final-year undergraduate students completing their degree before the program concludes.
  • Professionals with foundations in data science and Python looking to transition into AI/ML engineering roles.
Admission Process Simple, structured steps from application to enrollment
  1. Application Submission: Complete a short online application with academic/professional details.
  2. Profile Review: Selected applicants receive official admission confirmation.
  3. Seat Confirmation: Reserve your seat with INR 10,000.
  4. Fee Completion: Pay the remaining fee within 7 days of confirmation or before program start, whichever is earlier.
Learner Assistance Advisor support throughout your admission journey

Program advisors are available 7 days a week, 10:00 AM to 7:00 PM.

Email: hello@42learn.com

Phone: 080 4736 3406

Disclaimer: Outcome, career progression, and salary information is indicative only; individual results vary by background, experience, and market conditions. Certificates/credits are governed by the issuing institution's policies where external partners are involved.