Machine Learning

Mastering Machine Learning: From Basics to Advanced Applications

This comprehensive course provides an in-depth exploration of Machine Learning (ML), equipping learners with the skills to design, build, and deploy intelligent systems. Starting with foundational concepts, the course covers key techniques such as supervised and unsupervised learning, reinforcement learning, and neural networks. Participants will gain hands-on experience with real-world datasets, implementing algorithms like regression, classification, clustering, and decision trees using popular tools such as Python, TensorFlow, and Scikit-learn.

The curriculum also dives into advanced topics like deep learning, model optimization, and natural language processing, making it suitable for both beginners and professionals. By the end of the course, learners will be capable of solving complex problems, developing innovative AI solutions, and contributing to cutting-edge projects in industries such as finance, healthcare, e-commerce, and more.

Mastering Machine Learning: From Basics to Advanced Applications

We get it—life can be unpredictable. That’s why our program is built to fit around your schedule, not the other way around. Whether you need to catch up on missed classes, take a break for personal reasons, or just want to revisit the material, we’re here to make learning work for you.

missed a class ?

Watch the recording later, with teaching assistants available to solve your doubts

Work / family needs time?

Pause your course and restart a month later with the next batch.

Have doubts?

Get them resolved over text / video by our expert teaching assistants!

Want to revise?

Access assignments/notes lifelong and recordings upto 6 months post course completion

Easy Registration

In <2 minutes, make a new account or login using social media / Interviewbit

Quick Evaluation

Simple 30 minutes MCQ test, focused on aptitude and basic coding to find the right course for you

Enroll in your course

Sign up with our various EMI options to swiftly kickstart your learning journey

Upgrade your batch

Want a shorter course? Take the coding challenge after enrollment

Why Enroll in This Course?

Hands-on learning with Python, TensorFlow, and Scikit-learn
Real-world datasets and industry-focused projects
Learn from experts and gain global certification readiness
Deploy AI models for practical business solutions

Machine Learning Curriculum

Module 1: Introduction to Machine Learning

What is Machine Learning? Overview and applications
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Understanding AI, ML, and Deep Learning differences
Setting up the ML environment (Python, Jupyter, Anaconda)

Module 2: Data Preprocessing & Feature Engineering

Collecting and cleaning data
Handling missing values, outliers, and categorical data
Feature scaling and transformation techniques
Feature selection and dimensionality reduction (PCA, LDA)

Module 3: Supervised Learning – Regression & Classification

Regression models: Linear Regression, Polynomial Regression, Ridge & Lasso Regression
Classification models: Logistic Regression, Decision Trees, Random Forest, SVM, KNN
Model evaluation techniques (Confusion Matrix, Precision-Recall, ROC-AUC)
Hyperparameter tuning and optimization

Module 4: Unsupervised Learning – Clustering & Association

K-Means Clustering & Hierarchical Clustering
DBSCAN and Gaussian Mixture Models
Principal Component Analysis (PCA) for dimensionality reduction
Market Basket Analysis & Association Rule Learning (Apriori, FP-Growth)

Module 5: Neural Networks & Deep Learning

Introduction to Artificial Neural Networks (ANN)
Building Deep Neural Networks (DNN) using TensorFlow and Keras
Convolutional Neural Networks (CNN) for image processing
Recurrent Neural Networks (RNN) for time-series and NLP

Module 6: Reinforcement Learning & Advanced ML Techniques

Introduction to Reinforcement Learning (Markov Decision Processes)
Q-Learning and Deep Q-Networks (DQN)
Generative Adversarial Networks (GANs)
Transfer Learning & AutoML

Module 7: Natural Language Processing (NLP)

Text preprocessing and tokenization
Sentiment analysis and text classification
Named Entity Recognition (NER)
Transformers & Large Language Models (BERT, GPT)

Module 8: Model Deployment & Real-World Applications

Deploying models using Flask & FastAPI
Using TensorFlow Serving & Docker for deployment
ML model deployment on cloud (AWS, GCP, Azure)
Real-world case studies (finance, healthcare, e-commerce)

Module 9: Capstone Project & Certification Preparation

End-to-end ML project with real-world data
Model evaluation, fine-tuning, and performance optimization
Preparing for industry certifications (TensorFlow Developer, AWS ML, Microsoft AI)
Resume building & job interview preparation

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