Machine Learning Engineering

Machine Learning Engineering — Learning Path Steps

1. Step 1: Understand the Basics of Machine Learning
   • Supervised Learning
   • Unsupervised Learning
   • Reinforcement Learning
   • Feature Engineering
   • Model Evaluation
2. Step 2: Learn Programming and Data Manipulation
   • Python Programming
   • NumPy
   • Pandas
   • Data Cleaning
   • Data Preprocessing
3. Step 3: Dive into Machine Learning Algorithms
   • Linear Regression
   • Logistic Regression
   • Decision Trees
   • Random Forests
   • Support Vector Machines
   • K-Nearest Neighbors
   • Naive Bayes
   • Neural Networks
   • Gradient Boosting
4. Step 4: Understand Model Evaluation and Validation
   • Train-Test Split
   • Cross-Validation
   • Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
   • Overfitting and Underfitting
   • Hyperparameter Tuning
5. Step 5: Explore Advanced Topics in Machine Learning
   • Dimensionality Reduction
   • Ensemble Learning
   • Deep Learning
   • Natural Language Processing
   • Recommender Systems
   • Time Series Analysis
   • Transfer Learning
6. Step 6: Gain Practical Experience with Real-World Projects
   • Data Collection and Preparation
   • Feature Selection
   • Model Training and Evaluation
   • Deployment and Monitoring
   • Iterative Improvement
7. Step 7: Stay Updated and Continuously Learn
   • Reading Research Papers
   • Participating in Kaggle Competitions
   • Following Machine Learning Blogs and Forums
   • Attending Conferences and Workshops
   • Experimenting with New Techniques and Algorithms