Machine learning Roadmap

Machine Learning Basics:

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning
  

Machine Learning Algorithms:

• Linear Regression

• Logistic Regression

• Decision Trees

• Random Forests

• Support Vector Machines (SVM)

• k-Nearest Neighbors (kNN)

• Naive Bayes

• Clustering Algorithms (e.g., k-Means, Hierarchical Clustering)

• Dimensionality Reduction Techniques (e.g., PCA)

Advanced Machine Learning Techniques:

• Ensemble Methods (e.g., Bagging, Boosting)

• Regularization Techniques (e.g., L1/L2 Regularization)

• Optimization Algorithms (e.g., Gradient Descent, Stochastic Gradient Descent)

• Cross-validation and Model Evaluation Metrics

Natural Language Processing (NLP):

• Text Preprocessing

• Word Embeddings (e.g., Word2Vec, GloVe)

• Named Entity Recognition (NER)

• Sentiment Analysis

• Text Classification

Time Series Analysis:

• Time Series Preprocessing

• Seasonality and Trend Analysis

• ARIMA Models

• Exponential Smoothing Methods

Model Deployment and Scalability:

• API Development (e.g., Flask, FastAPI)

• Containerization (e.g., Docker)

Model Interpretability and Explainability:

• SHAP Values

• LIME (Local Interpretable Model-agnostic Explanations)

• wandb library

• Responsible AI Practices