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Machine Learning & Shape the Future with Intelligent Systems
Master practical machine learning skills including data modeling, predictive analytics, algorithms, and automation to build intelligent, real-world solutions.
Machine Learning That Powers Data-Driven Innovation
Our machine learning programs equip individuals and organizations with practical skills to build predictive models, analyze data patterns, automate decision-making, and deploy scalable intelligent solutions.
Duration
3 Months
Sessions
36
Classes Days
Mon, Wed, Fri
Summary Of The Course
This Advanced Machine Learning Professional course is designed for learners who want to achieve an industry-ready level and already possess a basic understanding of programming, data, or technical concepts. The course focuses on core machine learning foundations, data preparation, model training, evaluation techniques, deployment workflows, and real-world applications to build scalable, data-driven intelligent systems.
Machine Learning Model Design & Training
Data Analysis & Predictive Modeling Projects
End-to-End ML Pipelines & Automation
Client-Style, Portfolio-Ready ML Projects
Learners will complete hands-on projects covering dataset preparation, feature engineering, model development, evaluation, optimization, and deployment—resulting in practical, portfolio-ready machine learning solutions.
Machine Learning Fundamentals & Algorithms
Supervised & Unsupervised Learning Techniques
Data Preprocessing, Feature Engineering & Model Evaluation
Model Optimization, Validation & Performance Tuning
Real-World Machine Learning Use Cases
Learners will gain a strong foundation in machine learning concepts, understand how to select and train appropriate models, evaluate performance, and apply ML techniques to solve practical, data-driven problems.
Python for Machine Learning
NumPy, Pandas & Data Handling
Scikit-learn for Model Building
Data Visualization & Exploratory Analysis
Model Deployment Basics & ML Workflows
Learners will work hands-on with industry-standard tools and libraries used by machine learning professionals, developing practical skills required for real-world ML projects and deployment-ready solutions.
Automated Machine Learning (AutoML) Concepts
Model Training, Testing & Evaluation Automation
Data Pipelines & Workflow Automation
Model Monitoring & Continuous Improvement
Introduction to MLOps & Scalable ML Systems
Learners will understand how machine learning workflows can be automated to improve efficiency, consistency, and scalability. The course introduces automated pipelines, model lifecycle management, and continuous learning approaches used in modern ML-driven organizations.
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📚 Table of Contents
Orientation & Machine Learning Foundations
Session Subjects Discussed
Introduction to Machine Learning & Industry Trends
Difference Between AI, Machine Learning & Deep Learning
Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Real-World Machine Learning Applications
Data-Driven Decision Making
Ethics, Bias & Responsible Machine Learning
Machine Learning Career Paths
Python & Data Fundamentals for Machine Learning
Session Subjects Discussed
Python Essentials for Machine Learning
Working with NumPy & Pandas
Data Cleaning & Preprocessing Techniques
Exploratory Data Analysis (EDA)
Handling Missing Data & Outliers
Supervised Learning – Regression Models
Session Subjects Discussed
Linear & Multiple Regression
Model Assumptions & Evaluation Metrics
Feature Selection & Engineering
Overfitting & Underfitting
Real-World Regression Use Cases
Supervised Learning – Classification Models
Session Subjects Discussed
Logistic Regression
Decision Trees & Random Forest
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Classification Metrics & Confusion Matrix
Unsupervised Learning Techniques
Session Subjects Discussed
Clustering Concepts & Applications
K-Means & Hierarchical Clustering
Dimensionality Reduction (PCA)
Anomaly Detection
Business & Industry Use Cases
Model Optimization & Evaluation
Session Subjects Discussed
Model Validation Techniques
Hyperparameter Tuning
Cross-Validation
Performance Optimization
Model Comparison & Selection
ML Automation, Pipelines & MLOps Basics
Session Subjects Discussed
Automated ML Workflows
Data & Model Pipelines
Introduction to MLOps
Model Versioning & Monitoring
Continuous Learning Systems
Capstone Projects & Deployment
Session Subjects Discussed
End-to-End Machine Learning Project
Real-World Dataset Handling
Model Deployment Basics
Client-Style ML Assignments
Portfolio-Ready Final Project
👩🏻💻 Others Course
📚 Table of Contents
Orientation & Machine Learning Foundations
Session Subjects Discussed
Introduction to Machine Learning & Industry Trends
Difference Between AI, Machine Learning & Deep Learning
Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Real-World Machine Learning Applications
Data-Driven Decision Making
Ethics, Bias & Responsible Machine Learning
Machine Learning Career Paths
Python & Data Fundamentals for Machine Learning
Session Subjects Discussed
Python Essentials for Machine Learning
Working with NumPy & Pandas
Data Cleaning & Preprocessing Techniques
Exploratory Data Analysis (EDA)
Handling Missing Data & Outliers
Supervised Learning – Regression Models
Session Subjects Discussed
Linear & Multiple Regression
Model Assumptions & Evaluation Metrics
Feature Selection & Engineering
Overfitting & Underfitting
Real-World Regression Use Cases
Supervised Learning – Classification Models
Session Subjects Discussed
Logistic Regression
Decision Trees & Random Forest
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Classification Metrics & Confusion Matrix
Unsupervised Learning Techniques
Session Subjects Discussed
Clustering Concepts & Applications
K-Means & Hierarchical Clustering
Dimensionality Reduction (PCA)
Anomaly Detection
Business & Industry Use Cases
Model Optimization & Evaluation
Session Subjects Discussed
Model Validation Techniques
Hyperparameter Tuning
Cross-Validation
Performance Optimization
Model Comparison & Selection
ML Automation, Pipelines & MLOps Basics
Session Subjects Discussed
Automated ML Workflows
Data & Model Pipelines
Introduction to MLOps
Model Versioning & Monitoring
Continuous Learning Systems
Capstone Projects & Deployment
Session Subjects Discussed
End-to-End Machine Learning Project
Real-World Dataset Handling
Model Deployment Basics
Client-Style ML Assignments
Portfolio-Ready Final Project
👩🏻💻 Related Course
Why Learn Machine Learning?
Build in-demand machine learning skills, transform data into predictive insights, and develop intelligent models that power automation, innovation, and high-growth career opportunities across industries.
High-Demand & Future-Proof Career
Machine learning skills are in strong global demand across industries like IT, healthcare, finance, manufacturing, and AI startups—opening doors to high-paying and long-term career opportunities.
Data-Driven Problem Solving Skills
Machine learning enables you to analyze data, build predictive models, and automate decision-making—helping organizations make smarter, faster, and more accurate decisions.
Innovation, Automation & Growth
With machine learning, you can create intelligent systems that automate processes, improve efficiency, and drive innovation—making you a valuable asset in the future digital economy.
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FAQ About Techverse
Get answers about our NAVTTC, PSDF & TEVTA approved courses, job placement support, and career transformation programs
What is the Machine Learning program at TechVerse by BGMC?
The Machine Learning program at TechVerse by BGMC is a career-focused training designed to equip learners with practical skills in data analysis, predictive modeling, algorithm development, and real-world machine learning applications.
Where is the Machine Learning training offered?
The Machine Learning training is offered through flexible learning modes, including online and instructor-led sessions, making it accessible to learners locally and internationally.
Who can enroll in the Machine Learning course?
This course is suitable for students, fresh graduates, IT professionals, engineers, analysts, and anyone with a basic understanding of programming or data who wants to build a career in machine learning.
What are the benefits of learning Machine Learning at TechVerse?
Learners gain in-demand ML skills, hands-on project experience, exposure to real datasets, portfolio-ready projects, and career-oriented training aligned with modern industry requirements.
Is the Machine Learning program approved or recognized?
Yes. The Machine Learning program is delivered under TechVerse by BGMC and follows globally accepted industry practices, frameworks, and skill standards relevant to the AI and data science domain.
What makes TechVerse’s Machine Learning course different?
TechVerse focuses on practical learning, real-world projects, expert mentorship, modern tools, and job-oriented skill development—ensuring learners are industry-ready, not just theory-trained.