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Data Science & Machine Learning

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Data Science & Machine Learning — Learning Path Steps

  1. Step 1: Learn the Basics of Data Science
    • Introduction to Data Science
    • Statistics and Probability
    • Data Manipulation and Cleaning
    • Data Visualization
  2. Step 2: Understand Machine Learning Fundamentals
    • Supervised Learning
    • Unsupervised Learning
    • Model Evaluation and Validation
    • Feature Engineering
  3. Step 3: Learn Machine Learning Algorithms
    • Linear Regression
    • Logistic Regression
    • Decision Trees and Random Forests
    • Support Vector Machines
    • Naive Bayes
    • K-Nearest Neighbors
    • Clustering Algorithms
    • Dimensionality Reduction
    • Ensemble Methods
  4. Step 4: Gain Practical Experience
    • Working with Real-world Datasets
    • Implementing Machine Learning Models
    • Model Evaluation and Hyperparameter Tuning
    • Handling Imbalanced Data
    • Dealing with Missing Data
  5. Step 5: Deep Dive into Advanced Topics
    • Deep Learning and Neural Networks
    • Natural Language Processing
    • Reinforcement Learning
    • Time Series Analysis
    • Recommendation Systems