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🎓Personalized Learning, Tailored to Your Needs and Knowledge

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What You'll Learn

Fundamentals of machine learning, including supervised, unsupervised, and reinforcement learning.
Key machine learning algorithms like linear regression, decision trees, random forests, and K-means clustering.
Practical use of Python and core libraries (Scikit-Learn, NumPy, Pandas, Matplotlib) for model development.
Data preprocessing, model evaluation, and hyperparameter tuning.
Real-world applications of machine learning in various industries like finance, healthcare, and marketing.

Skills You'll Gain

Machine Learning AlgorithmsData AnalysisPython ProgrammingAI Prompt Engineering\n

Self Learning

Module 1: Introduction to Machine Learning

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What is Machine Learning?

Learning Objectives: Define Machine Learning and understand the difference between AI, ML, and Deep Learning.
Resource Links:
- What is Machine Learning?
- Define ML
Estimated Time: 1 hour

Types of Machine Learning

Learning Objectives: Distinguish between supervised, unsupervised, and reinforcement learning
Resource Links:
- Types of Machine Learning
- Machine Learning Algorithms
Estimated Time: 1 hour

Applications of Machine Learning

Learning Objectives: Explore various real-world applications of ML and understand the impact of ML on different industries.
Resource Links:
- Machine Learning Insights
- History of ML
Estimated Time: 2 hours

Module 2: Python for Machine Learning

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Module 3: Data Preprocessing

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Module 4: Supervised Learning

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Module 5: Unsupervised Learning

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Module 6: Model Evaluation and Improvement

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