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machine learning bootcamp

Edurush’s Machine Learning Bootcamp is an intensive, career-focused program designed to build your expertise in machine learning from the ground up. This hands-on machine learning course covers essential topics like supervised and unsupervised learning, model building, Python programming, and real-time data projects. With expert mentors, industry-relevant case studies, and practical training, you’ll gain the skills needed to solve real-world problems using AI and ML techniques. Whether you’re a student or working professional, this machine learning bootcamp prepares you for high-demand roles in data science and artificial intelligence. Join Edurush to accelerate your career with the most comprehensive machine learning course in Hyderabad.

  • 2 Months | 66 Hours
  • 60,000 Enrollments
  • Certificate
  • 4.8
online life training via LMS

online life training via LMS

offline life training at campus

offline life training at campus

hansome trainee

hansome trainee

Pricing

₹1,32,000 (10% OFF)

+GST (18% )

₹1,20,000

machine learning bootcamp

Edurush’s Machine Learning Bootcamp is an intensive, career-focused program designed to build your expertise in machine learning from the ground up. This hands-on machine learning course covers essential topics like supervised and unsupervised learning, model building, Python programming, and real-time data projects. With expert mentors, industry-relevant case studies, and practical training, you’ll gain the skills needed to solve real-world problems using AI and ML techniques. Whether you’re a student or working professional, this machine learning bootcamp prepares you for high-demand roles in data science and artificial intelligence. Join Edurush to accelerate your career with the most comprehensive machine learning course in Hyderabad.

  • Duration:4 Months | 160+ hours
  • 60,000 Enrollments
  • Certificate
  • Internship
  • Fastrack Weekday/Weekend Program
  • 4.8
Online Live Training Via LMS

Online Live Training Via LMS

Offline Live Training at Campus

Offline Live Training at Campus

Hands on
Training

Pricing

₹90,000 (16% OFF)

+GST (18%)

₹75,000

Course Journey

Kick start your journey

  • Orientation by Instructors
  • Meet Your Program Instructors
  • Meet Your Peers

Master full stack by solving real problems

  • Build Real World Projects
  • 1:1 Doubt Resolution
  • Meet Industry Experts
  • Interview Preparation Assistance

Career Advancement

  • Resume and Profile Creation
  • Mock Interviews
  • Soft Skills Training

Catch the Eye of Your Dream Companies

  • Daily Job Alerts
  • Alumni Referrals
  • Industry Insights

Nail the Interview for your dream job

  • Gain company insights
  • Apply for higher offers

Kick start your journey

  • Orientation by Instructors
  • Meet Your Program Instructors
  • Meet Your Peers

Master full stack by solving real problems

  • Build real world projects
  • 1:1 Doubt Resolution
  • Meet industry experts
  • Interview Preparation Assistance

Career Advancement

  • Resume and Profile Creation
  • Mock interviews
  • Soft skill training

Catch the Eye of Your Dream Companies

  • Daily Job Alerts
  • Alumni Referrals
  • Industry Insights

Nail the Interview for your dream job

  • Gain company insights
  • Apply for higher offers

Course curriculum

Module 1 - Introduction to Machine Learning
  • What is Machine Learning?
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Machine Learning vs. Traditional Programming
  • Real-World Applications of Machine Learning
  • Setting Up the ML Development Environment (Python, Jupyter Notebook)
  • Introduction to Python for ML (NumPy, Pandas, Matplotlib)
  • Understanding Data, Features, and Labels
Module 2 - Data Preprocessing and Cleaning
  • Understanding Data Quality and Preprocessing
  • Handling Missing Data (Mean, Median, Mode, Imputation)
  • Data Normalization and Standardization
  • Handling Categorical Data (One-Hot Encoding, Label Encoding)
  • Feature Scaling (Min-Max, Standard Scaler)
  • Removing Outliers and Handling Imbalanced Data
  • Data Splitting: Train, Validation, and Test Sets
Module 3 - Exploratory Data Analysis (EDA)
  • Understanding Data Distribution and Statistics
  • Data Visualization with Matplotlib and Seaborn
  • Identifying Patterns and Correlations
  • Univariate, Bivariate, and Multivariate Analysis
  • Feature Engineering and Feature Selection
  • Dimensionality Reduction (PCA, t-SNE)
  • Detecting and Handling Multicollinearity
Module 4 - Supervised Learning – Regression
  • Introduction to Regression Models
  • Simple and Multiple Linear Regression
  • Polynomial Regression
  • Ridge and Lasso Regression
  • Evaluating Regression Models (MSE, RMSE, R² Score)
  • Bias-Variance Tradeoff and Overfitting
  • Hyperparameter Tuning for Regression Models
Module 5- Supervised Learning – Classification
  • Introduction to Classification Models
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (KNN)
  • Performance Metrics (Accuracy, Precision, Recall, F1-Score, ROC Curve)
  • Handling Class Imbalance (SMOTE, Class Weights)
Module 6- Unsupervised Learning – Clustering
  • Introduction to Unsupervised Learning
  • k-Means Clustering
  • Hierarchical Clustering
  • DBSCAN Clustering Algorithm
  • Evaluating Clustering Performance
  • Feature Scaling for Clustering Models
  • Hands-on: Customer Segmentation
  • Dimensionality Reduction in Clustering
  •  
Module 7- Unsupervised Learning – Association & Anomaly Detection
  • Market Basket Analysis and Association Rules
  • Apriori Algorithm
  • FP-Growth Algorithm
  • Outlier Detection Techniques
  • Isolation Forest for Anomaly Detection
  • Local Outlier Factor (LOF)
  • Applications of Anomaly Detection
Module 8- Feature Engineering & Selection
  • Importance of Feature Engineering
  • Handling Categorical and Numerical Features
  • Encoding Techniques (Label Encoding, One-Hot Encoding)
  • Feature Importance using Random Forests
  • Recursive Feature Elimination (RFE)
  • Feature Extraction (PCA, LDA)
  • Automating Feature Selection (Boruta, SHAP)
Module 9-Model Evaluation & Hyperparameter Tuning
  • Cross-Validation Techniques (K-Fold, Stratified K-Fold)
  • Grid Search vs. Randomized Search
  • Bayesian Optimization
  • Regularization Techniques (L1, L2)
  • Early Stopping in ML Models
  • Comparing Multiple Models Effectively
  • Model Interpretability (LIME, SHAP)
  • Hands-on: Hyperparameter Tuning in a Kaggle Competition
Module 10- Introduction to Deep Learning
  • What is Deep Learning?
  • Neural Networks vs. Traditional ML Models
  • Perceptrons and Activation Functions
  • Forward and Backpropagation
  • Introduction to TensorFlow and Keras
  • Building a Simple Neural Network
  • Loss Functions and Optimizers
Module 11- Advanced Neural Networks
  • Convolutional Neural Networks (CNNs) for Image Processing
  • Recurrent Neural Networks (RNNs) for Sequential Data
  • Long Short-Term Memory (LSTMs) for Time Series Data
  • Transformers and Self-Attention Mechanism
  • Transfer Learning with Pretrained Models
  • GANs (Generative Adversarial Networks) for Synthetic Data Generation
  • Deploying Deep Learning Models
Module 12- Time Series Forecasting
  • Introduction to Time Series Analysis
  • Stationarity and Differencing
  • ARIMA and SARIMA Models
  • Exponential Smoothing Models
  • LSTMs for Time Series Data
  • Feature Engineering for Time Series Forecasting
  • Evaluating Time Series Models
Module 13- Natural Language Processing (NLP)
  • Introduction to NLP and Text Processing
  • Tokenization, Lemmatization, and Stemming
  • TF-IDF and Word Embeddings (Word2Vec, GloVe)
  • Sentiment Analysis with NLP
  • Named Entity Recognition (NER)
  • Transformers and BERT Models
  • Deploying NLP Models
Module 14- Database Integration
  • Entity Framework Core
  • Setting Up EF Core
  • Code-First Migrations
  • Working with DbContext
  • Connecting to SQL Databases
  • Configuration
  • Performing CRUD Operations
Module 15 - Reinforcement Learning
  • Basics of Reinforcement Learning
  • Markov Decision Processes (MDP)
  • Q-Learning and Deep Q-Networks (DQN)
  • Policy Gradient Methods
  • OpenAI Gym for RL Experiments
  • Monte Carlo Methods
  • Hands-on: Game Playing AI
  • Ethical Considerations in RL
Module 16- Model Deployment & MLOps
  • Introduction to Model Deployment
  • Deploying Models with Flask and FastAPI
  • Dockerizing Machine Learning Models
  • CI/CD for ML Pipelines
  • Cloud Deployment with AWS/GCP
  • Monitoring and Logging ML Models
  • Hands-on: Deploying ML Model on Heroku
  • Scaling ML Models in Production
Module 17- Additional Resources
  • Official Documentation
  • Recommended Books
  • Online Tutorials and Courses
  • Community and Support

Our Instructors

1250304892_1218270-3697 (1)

Ram.L

Full stack developer

diverse-business-teammates-gather-together-share-information-plans_709984-23511

priya

Full stack developer

Course Benefits

edurush

Free resources

Other courses

Structured + problem solving based

Fastest 1:1 doubt support

Integrated prep platform

Profiles highlighted on Naukri

Your dream role, faster and with confidence!

100%

Average role, under-confident

others

Course Benefits

edurush

Free resources

Other courses

Integrated & Detailed Curriculum

Rapid 1:1 Doubt Resolution

Unified Learning Platform

Hands-On Practical Projects

Highlighted Profiles on Naukri, Indeed

Industry-Relevant Skills

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FAQ’s Of Full Stack Development

What are the prerequisites for learning Machine Learning Bootcamp?

Basic programming knowledge (preferably Python), understanding of mathematics (linear algebra, probability, statistics), familiarity with data structures, problem-solving skills, and a keen interest in data-driven decision-making are essential for a Machine Learning Bootcamp.

How much time will it take to learn Machine Larning Bootcamp?

The duration of our program is 5 months (220 hours). However, the actual time to become proficient may vary depending on your prior experience and dedication to practice.

Is there any certification for Machine Learning Bootcamp?

Yes, you will receive a certification upon successful completion of the program, validating your skills in full stack development.

Why is Machine Learning Bootcamp better than specialized front-end or back-end development?

A Machine Learning Bootcamp offers data-driven problem-solving skills, automation expertise, AI integration, and broader career opportunities, surpassing specialized front-end or back-end development in impact and future growth potential.

How do I get my doubts resolved?

You can get your doubts resolved through 1:1 sessions with instructors, interactive live classes, and dedicated support via the learning management system (LMS).

Where will the classes be conducted? What are the course timings?

Classes are conducted online via the LMS and offline at EDURUSH campuses. Course timings vary based on the batch you enroll in and can include weekday and weekend options to accommodate different schedules.

How will I benefit from Industry Mentors?

Industry mentors provide valuable insights, guidance, and feedback based on their real-world experience. They can help you prepare for interviews, enhance your understanding of industry practices, and provide networking opportunities.

How many domain expert sessions & mock interviews will I have?

he number of domain expert sessions and mock interviews depends on the plan you choose. Generally, higher-tier plans offer more sessions and interviews. Contact EDURUSH for specific details on each plan.

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