Veda IT

Who Should Join Machine Learning with Python Course?

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

Keyskills of Machine Learning with Python Developer

A Certificate Program in Machine Learning with Python teaches key skills like Python programming, data manipulation with Pandas and NumPy, and model building using Scikit-learn. Key areas include supervised and unsupervised learning, data preprocessing, feature engineering, and model evaluation. Proficiency in using libraries for visualization, as well as deploying models with Flask or cloud platforms, is essential for hands-on machine learning experience.

Key Features

Covers supervised and unsupervised learning, model evaluation, and deployment.
Real-world projects to develop practical skills in machine learning.
Learn from experienced ML practitioners and Python developers.
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 with Python at Veda IT is a comprehensive course designed to provide students with practical and theoretical knowledge of machine learning concepts, using Python as the programming foundation. This program is ideal for aspiring data scientists, IT professionals, and data analysts who want to learn how to develop and deploy machine learning models to solve real-world problems. Covering essential topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and hyperparameter tuning, the course emphasizes hands-on learning through coding labs and projects with Python libraries like Scikit-Learn, Pandas, and TensorFlow.

Throughout the program, students will develop a solid understanding of how to build, optimize, and deploy machine learning models, preparing them for roles in data science, machine learning engineering, or further studies in AI and advanced analytics.

Modules Covered

Overview of Machine Learning and Its Applications
Setting Up Python Environment (Jupyter, Scikit-Learn, TensorFlow)
Data Collection, Cleaning, and Preprocessing
Feature Engineering and Selection Techniques
Splitting Data for Training and Testing
Exploratory Data Analysis (EDA) with Pandas
Mini Project: Data Cleaning and Exploration

Linear and Polynomial Regression for Prediction
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 Regression or Classification

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

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

Learning Path

Introduction to Machine Learning and Python Basics:

Understand the fundamentals of machine learning and get started with Python programming for data analysis.

Supervised Learning - Regression and Classification:

Master algorithms like linear regression, logistic regression, decision trees, and support vector machines for predictive modeling.

Unsupervised Learning and Dimensionality Reduction:

Explore clustering techniques, principal component analysis (PCA), and other methods to uncover patterns in data.

Model Tuning, Advanced Techniques, and Deployment:

Optimize models with hyperparameter tuning, explore ensemble methods, and learn to deploy machine learning solutions.

Mini Projects for Hands-On Experience:

Apply your knowledge to small, real-world projects to gain practical experience

Capstone Project and Career Guidance:

Develop a complete machine learning solution and receive expert career coaching to excel in data science and machine learning roles.

Potential Roles

Machine Learning Engineer
Data Scientist (Python and ML)
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 with Python

Upon successful completion of the Certificate Program in Machine Learning with Python, you will receive a certificate from Veda IT, validating your skills in data preprocessing, model building, and machine learning model deployment.

Enroll Now

Design and Developed by Colourmoon

Machine Learning with Python

Who Should Join Machine Learning with Python Course?

Job Switchers

Working Professionals

Engineering Graduates

University Students

Entry-Level Candidates

Keyskills of Machine Learning with Python Developer

Key Features

What you'll learn

Modules Covered

Module 1: Introduction to Machine Learning and Python Basics 3 hours

Module 2: Supervised Learning - Regression and Classification 3 hours

Module 3: Unsupervised Learning and Dimensionality Reduction 3 hours

Module 4: Model Tuning, Advanced Techniques, and Deployment 3 hours

Learning Path

Potential Roles

Certificate Program in Machine Learning with Python