Veda IT

Who Should Join ML Course?

Job Switchers
Working Professionals
Engineering Graduates
University Students
Entry-Level Candidates

Keyskills of ML Developer

A Certificate Program in Machine Learning (ML) develops key skills such as proficiency in programming languages like Python or R and a strong foundation in statistics, probability, and linear algebra. Core skills include understanding supervised and unsupervised learning, model evaluation, and optimization techniques. Expertise in ML libraries and frameworks like Scikit-learn, TensorFlow, or PyTorch is essential.

Key Features

Covers supervised and unsupervised learning, model selection, and evaluation.
Real-world projects to develop practical skills in machine learning.
Learn from experienced ML professionals and data scientists.
Offline classes with optional online support.
Recognized certificate from Veda IT upon successful course completion.

What you'll learn

The Certificate Program in Machine Learning (ML) at Veda IT is a comprehensive course designed to provide students with a strong foundation in machine learning algorithms, data preprocessing, model building, and performance evaluation. This program is ideal for IT professionals, data enthusiasts, and students who want to gain expertise in building predictive models and applying machine learning techniques to solve real-world problems. The curriculum covers essential machine learning concepts such as supervised and unsupervised learning, feature engineering, model selection, and evaluation, with hands-on projects using popular tools like Python, Scikit-Learn, and TensorFlow.

Through project-based learning, students will gain practical experience in developing models for various applications, including classification, regression, and clustering. By the end of this program, graduates will be prepared for roles in data science, machine learning engineering, or further studies in AI and deep learning.

Modules Covered

Overview of Machine Learning and Its Applications
Setting Up Python and ML Libraries (Scikit-Learn, TensorFlow)
Data Collection, Cleaning, and Preprocessing
Feature Selection and Engineering
Splitting Data for Training and Testing
Basic Data Analysis and Visualization
Mini Project: Data Cleaning and Exploration

Linear Regression and Polynomial Regression
Logistic Regression for Classification
K-Nearest Neighbors (KNN) Algorithm
Decision Trees and Random Forests
Support Vector Machines (SVM)
Model Evaluation Metrics (Accuracy, Precision, Recall)
Mini Project: Building a Predictive Model for Classification or Regression

Introduction to Unsupervised Learning
Clustering Algorithms (K-Means, Hierarchical Clustering)
Principal Component Analysis (PCA) for Dimensionality Reduction
Anomaly Detection Techniques
Association Rule Learning (Apriori and Eclat)
Mini Project: Customer Segmentation or Anomaly Detection

Hyperparameter Tuning with Grid Search and Random Search
Cross-Validation for Model Evaluation
Introduction to Ensemble Methods (Bagging, Boosting)
Model Deployment with Flask and Docker
Monitoring Model Performance in Production
Capstone Project: End-to-End Machine Learning Application Deployment

Learning Path

Introduction to Machine Learning and Data Preprocessing

Learn the fundamentals of machine learning and techniques for cleaning and preparing data.

Supervised Learning - Regression and Classification

Master supervised learning algorithms like regression and classification for predictive modeling.

Unsupervised Learning and Dimensionality Reduction

Explore clustering techniques, principal component analysis (PCA), and other dimensionality reduction methods.

Model Tuning, Evaluation, and Deployment

Optimize models with hyperparameter tuning, evaluate performance, and deploy models to production.

Mini Projects for Hands-On Experience

Apply your machine learning skills to small-scale, real-world projects for practical learning.

Capstone Project and Career Guidance

Build an end-to-end machine learning project and receive career guidance to advance in the ML field.

Potential Roles

Machine Learning Engineer
Data Scientist (ML-Focused)
Data Analyst
AI Engineer
Predictive Modeler
Research Analyst (Machine Learning)

Start Date20/05/2025
Enrolled100
Lectures50
Skill LevelBasic
LanguageEnglish,Telugu
Quizzes10
CertificateYes
Pass Percentage100%

Certificate Program in Machine Learning (ML)

Upon successful completion of the Certificate Program in Machine Learning (ML), you will receive a certificate from Veda IT, validating your skills in machine learning model development, data analysis, and model deployment.

Enroll Now

Design and Developed by Colourmoon

ML

Who Should Join ML Course?

Job Switchers

Working Professionals

Engineering Graduates

University Students

Entry-Level Candidates

Keyskills of ML Developer

Key Features

What you'll learn

Modules Covered

Module 1: Introduction to Machine Learning and Data Preprocessing 3 hours

Module 2: Supervised Learning - Regression and Classification 3 hours

Module 3: Unsupervised Learning and Dimensionality Reduction 3 hours

Module 4: Model Tuning, Evaluation, and Deployment 3 hours

Learning Path

Potential Roles

Certificate Program in Machine Learning (ML)