Machine Learning (ML)

Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data.

The computer is fed with data and information. This gives these systems the ability to learn and enhance from experiences. The process happens without being explicitly programmed. In the below figure, you can easily understand the ecosystem of Machine Learning:

Contents

  1. ML High-Level Overview (by Example)
  2. Some Definitions
  3. Applications of ML
  4. Data Preparation In ML
  5. Models & Algorithms in ML
    1. Model & Algorithm
    2. Choosing a Model vs. Choosing an Algorithm
    3. Training a Model vs. Training an Algorithm
  6. Steps Of Creating A Model In ML
    1. Data Collecting
    2. Exploratory Data Analysis (EDA)
    3. Data Preprocessing
    4. Choosing a Model / an Algorithm
    5. Training a Model / an Algorithm
    6. Evaluating the Model
    7. Parameter Tuning
    8. Making Predictions
    9. Deploying the Model
  7. ML Models
    1. Supervised Learning
    2. Unsupervised Learning
    3. Semi-Supervised Learning
    4. Reinforcement Learning
  8. ML Algorithms
    1. Neural networks
    2. Linear regression
    3. Logistic regression
    4. Clustering
    5. Decision trees
    6. Random forests
    7. Classification Algorithms
    8. Regression Algorithms
    9. Clustering
  9. Other Important Concepts
    1. Ensemble Learning – Combines multiple models to improve performance (e.g., Bagging, Boosting)
    2. Deep Learning – A subset of ML using multi-layered neural networks (e.g., CNNs, RNNs, Transformers)
    3. Transfer Learning – Applying knowledge from one task to a related task
  10. ML Terminologies
  11. ML Metrics
  12. ML vs DL
  13. Fundamentals & Tools