Machine Learning Training

Duration: 4 Months

Suitable for: Beginners & Intermediates

Mode: Online & Classroom

Course Overview

Our comprehensive Machine Learning program equips you with the skills to develop systems that can automatically learn and improve from experience without being explicitly programmed. This intensive course covers fundamental ML algorithms, feature engineering, model evaluation, and deployment, preparing you for a career in one of the most in-demand fields in technology.

Led by industry professionals with extensive experience in machine learning applications, this training program combines theoretical foundations with hands-on projects, ensuring you gain practical experience building real-world ML solutions. By the end of the course, you'll have a professional portfolio demonstrating your ability to solve complex problems using state-of-the-art machine learning techniques.

Course Curriculum

Module 1: Foundations of Machine Learning
- Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Python Programming for ML
- Mathematics for ML (Linear Algebra, Calculus, Probability, Statistics)
- Data Structures and Algorithms
- Data Manipulation with NumPy and Pandas
- Data Visualization with Matplotlib and Seaborn
Module 2: Supervised Learning
- Linear Regression and Polynomial Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- K-Nearest Neighbors
- Naive Bayes Classifiers
- Gradient Boosting Algorithms (XGBoost, LightGBM)
Module 3: Unsupervised Learning
- Clustering Algorithms (K-Means, DBSCAN, Hierarchical)
- Dimensionality Reduction (PCA, t-SNE, UMAP)
- Anomaly Detection
- Association Rule Learning
- Gaussian Mixture Models
- Self-Organizing Maps
- Applications in Customer Segmentation and Recommendation Systems
Module 4: Feature Engineering
- Data Cleaning and Preprocessing
- Handling Missing Values
- Categorical Data Encoding
- Feature Scaling and Normalization
- Feature Selection Methods
- Feature Creation and Extraction
- Working with Time Series Data
Module 5: Model Evaluation and Validation
- Train-Test Split Strategies
- Cross-Validation Techniques
- Evaluation Metrics for Classification
- Evaluation Metrics for Regression
- Bias-Variance Tradeoff
- Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)
- Model Selection and Ensemble Methods
Module 6: Introduction to Deep Learning
- Neural Network Fundamentals
- Activation Functions
- Backpropagation Algorithm
- Building Models with TensorFlow and Keras
- Convolutional Neural Networks for Image Data
- Recurrent Neural Networks for Sequential Data
- Transfer Learning with Pre-trained Models
Module 7: Machine Learning in Production
- ML Project Pipeline and Lifecycle
- Model Deployment Strategies
- REST API Development with Flask
- Docker Containers for ML Applications
- Model Monitoring and Maintenance
- A/B Testing for ML Models
- MLOps Best Practices
Module 8: Specialized ML Applications
- Natural Language Processing Basics
- Time Series Forecasting
- Recommendation Systems
- Computer Vision Applications
- Fraud Detection Systems
- ML for Financial Markets
- Healthcare Applications
Module 9: Capstone Projects
- Predictive Analytics System
- Customer Segmentation and Targeting
- Recommendation Engine
- Time Series Forecasting Application
- Anomaly Detection System
- Portfolio Development & Documentation

Learning Outcomes

Develop end-to-end machine learning solutions from data processing to deployment
Apply appropriate algorithms for different types of machine learning problems
Engineer and select features to optimize model performance
Evaluate and validate machine learning models
Deploy and monitor machine learning models in production
Implement specialized ML applications in NLP, time series, and computer vision
Build a professional portfolio of machine learning projects

Career Opportunities

Machine Learning Engineer
Data Scientist
ML Operations Engineer
Business Intelligence Developer
Predictive Analytics Specialist
Recommendation Systems Engineer
ML Applications Developer

₹39,999

Inclusive of all taxes

160+ Hours of Training
6+ Real-world Projects
Industry-relevant Curriculum
Expert Instructors
Job Placement Assistance
Certificate of Completion
Access to Resources & Materials
Internship Opportunities

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Student Projects

Stock Market Prediction System

Time series forecasting application that predicts stock price movements using LSTM networks and technical indicators with configurable risk management parameters.

E-Commerce Recommendation Engine

Hybrid recommendation system combining collaborative filtering and content-based approaches to provide personalized product suggestions for online retail platforms.

Customer Segmentation Tool

Clustering-based application that identifies distinct customer segments and provides actionable insights for targeted marketing campaigns and product development.

Fraud Detection System

Real-time anomaly detection system that identifies suspicious financial transactions by analyzing patterns and user behavior with high accuracy and low false positives.

Student Success Stories

"The Machine Learning course at Shabnam Infosys completely transformed my career trajectory. Coming from a background in software development, I was looking to specialize in data-driven applications. The course structure was perfect - starting with strong fundamentals before progressing to advanced topics. The projects were challenging but incredibly rewarding, especially the recommendation system I built which is now part of my professional portfolio. Within weeks of completing the course, I secured a role as a Machine Learning Engineer at a fintech startup with a 40% salary increase."

Vikram Singh

ML Engineer at FinanceTech Solutions

"As someone without a programming background, I was initially intimidated by machine learning, but the instructors at Shabnam Infosys made the journey accessible and enjoyable. They broke down complex concepts into digestible modules and were always available to provide support when I struggled. The practical emphasis of the course was invaluable - I built five different ML applications during the training, each addressing real business problems. I'm now working as a Data Scientist at a retail analytics company, using the exact same techniques I learned in the course to drive business decisions."

Sneha Patel

Data Scientist at RetailMetrics

"I enrolled in the ML course to enhance my skills as a business analyst, but it ended up opening doors I hadn't even considered. The course perfectly balanced theory and application, with a focus on solving real business problems rather than just technical exercises. The capstone project where I developed a churn prediction model became a talking point in every interview. The career support team was exceptional, helping me revamp my resume and prepare for technical interviews. I'm now leading ML initiatives at my company, with a substantial promotion and the confidence to implement sophisticated analytics solutions."

Karthik Reddy

Lead Analytics Specialist at Global Services Inc.

Frequently Asked Questions

Do I need prior programming experience for this course?

Basic programming knowledge, particularly in Python, is recommended but not strictly required. We provide pre-course materials to help beginners learn Python fundamentals. The first module includes Python programming for ML, starting from the basics. However, if you have no programming experience at all, we suggest taking our Python fundamentals course first to ensure you can focus on learning machine learning concepts rather than struggling with coding basics.
How is this course different from the Data Science course?

While there is overlap, the Machine Learning course focuses specifically on developing predictive models and intelligent systems that can learn from data. It goes deeper into ML algorithms, feature engineering, and model deployment. The Data Science course is broader, covering data collection, cleaning, analysis, visualization, and basic ML, with more emphasis on extracting insights and storytelling with data. If you're specifically interested in building predictive systems and automated learning models, the ML course is more targeted to those goals.
What kind of projects will I build during the course?

Throughout the course, you'll work on progressively complex projects, including a credit risk prediction system, customer segmentation analysis, product recommendation engine, predictive maintenance application, time series forecasting for business metrics, sentiment analysis for customer feedback, and anomaly detection for fraud prevention. Your capstone project will be an end-to-end ML solution addressing a real business problem, which you'll design, develop, and deploy with guidance from instructors. All projects use real-world datasets and follow industry best practices for development and documentation.
Do you provide job placement after course completion?

Yes, we provide comprehensive placement assistance including resume building focused on highlighting ML projects and skills, interview preparation specific to machine learning roles (including technical problem-solving sessions), connections with our hiring partners looking for ML talent, and dedicated mentorship throughout your job search. Our placement success rate for ML course graduates is over 85% within three months of course completion. We maintain relationships with numerous companies across various industries that regularly hire our graduates for data science and ML positions.
Will this course cover deep learning in detail?

This course includes an introduction to deep learning fundamentals, covering neural networks, CNNs, and RNNs, providing enough knowledge to understand and implement basic deep learning models. However, if you're looking for comprehensive deep learning training, we recommend our specialized Deep Learning course after completing this one. The Machine Learning course focuses primarily on traditional ML algorithms, feature engineering, and deployment, which form the foundation of any data science career. Deep learning is introduced as one of several advanced techniques that build upon these core ML concepts.

Machine Learning