Machine Learning: Beginner
Introduction to Machine Learning: Beginner
Overview:
- Develop skills in model tuning, feature engineering, and using tools like scikit-learn and TensorFlow for predictive analytics.
- Duration: 1 Month
Topics Covered:
- Fundamentals of machine learning
- Supervised and unsupervised learning techniques
- Introduction to data preprocessing and feature engineering
- Basic algorithms: Linear Regression, Decision Trees, K-Means
- Model evaluation and performance metrics
Syllabus
Learning Outcome
- Understand core machine learning concepts, build basic models, and apply simple algorithms to analyze and predict data patterns.
Reviews
🛣️ Machine Learning Roadmap for Beginners
Begin your machine learning journey with essential concepts, Python skills, and practical knowledge of algorithms, tools, and workflows.
Machine Learning Beginner
Start your ML journey by learning the basics of ML, Python programming, data preprocessing, and probability & statistics.
Machine Learning Level 01
Advance your ML skills with supervised and unsupervised learning, model evaluation, and feature engineering.
Machine Learning Level 02
Master ensemble methods, advanced algorithms, optimization, and practical ML applications.
Internship
Work on real datasets and ML projects during a free, 1-year internship to apply knowledge in practice.
Mini Project
Apply ML concepts through guided mini projects involving real-world datasets and scenarios.
Expected Jobs
Step into the fast-growing machine learning industry with roles such as ML Engineer, Data Analyst, and ML Researcher.
🏆 Career Destinations
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