Description
Learn Machine Learning with Python through practical, industry-focused training designed for students, freshers, and professionals. This course helps you understand how machines learn from data and make intelligent predictions using powerful Machine Learning algorithms and Python libraries.
You will gain hands-on experience with Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, and Machine Learning models while working on real-world datasets and projects. From data preprocessing and visualization to supervised learning, regression, classification, and neural networks, this course gives you complete practical exposure to Machine Learning concepts.
Whether you want to become a Machine Learning Engineer, AI Developer, Data Analyst, or Python Developer, this course helps you build job-ready skills through hands-on learning and practical implementation.
What you will learn in this course?
- Python Programming Fundamentals
- Data Structures & File Handling in Python
- Data Analysis using NumPy & Pandas
- Data Visualization with Matplotlib & Seaborn
- Statistical Thinking & Data Analysis
- Supervised & Unsupervised Learning
- Classification & Regression Models
- Linear Regression & Logistic Regression
- Data Preprocessing Techniques
- Feature Scaling & Selection
- Decision Trees & Random Forest
- Bagging & Boosting Techniques
- Fundamentals of Neural Networks
- Real-World Machine Learning Projects
Benefits of Joining This Course
- Industry-Oriented Curriculum
- Hands-on Practical Learning
- Beginner-Friendly Training
- Real-World Projects & Case Studies
- Learn In-Demand Machine Learning Skills
- Expert Mentorship & Guidance
- Resume Building Support
- Interview Preparation Assistance
- Job-Oriented Skill Development
- Certification After Completion
Course Certificate
What You’ll Achieve
After completing this course, you will be able to:
- Build Machine Learning models using Python
- Analyze, clean, and preprocess datasets
- Create meaningful data visualizations and insights
- Apply supervised and unsupervised learning technique
- Build regression and classification models
- Perform feature engineering and model optimization
- Work with Decision Trees & Random Forest algorithms
- Understand Neural Network fundamentals
- Complete real-world Machine Learning projects
- Become industry-ready for ML & AI roles
Tools and Technologies You'll Learn:
Curriculum
- 13 Sections
- 59 Lessons
- 12 Weeks
- Module 1: Python Fundamentals6
- Module 2: Python File Handling & Data Structures6
- Module 3: Loops & Conditional Statements3
- Module 4: Data Analysis with NumPy3
- Module 5: Data Analysis with Pandas3
- Module 6: Data Visualization5
- Module 7: Statistical Thinking in Python4
- Module 8: Supervised & Unsupervised Learning7
- Module 9: Logistic Regression3
- Module 10: Linear Regression2
- Module 11: Data Preprocessing for Machine Learning7
- Module 12: Tree-Based Models5
- Module 13: Neural Networks & Projects5