Machine Learning with Python is a popular subject that has applications across various domains like data science, artificial intelligence, and more. Python is often the language of choice for machine learning due to its ease of use, extensive libraries, and vibrant community.

Basics

1. Environment Setup: Usually starts with Python installation and setup. You can also use environments like Jupyter notebooks for quick experiments.
2. Libraries: Key libraries include NumPy, pandas for data manipulation, and Matplotlib, Seaborn for data visualization. For machine learning, Scikit-Learn is commonly used.
3. Data Preprocessing: Involve cleaning the data, handling missing values, normalization, and so on.

Algorithms

1. Supervised Learning

• • Linear Regression
  • Decision Trees
  • Random Forest
  • SVM

1. Unsupervised Learning

• • K-means Clustering
  • Hierarchical Clustering
  • PCA

1. Neural Networks

• • Basic Perceptrons
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)

Steps to Build a Model

1. Data Collection
2. Data Preprocessing
3. Feature Engineering
4. Model Selection
5. Model Training
6. Model Evaluation
7. Model Deployment

Best Practices

• Always perform data exploration before jumping into model building.
• Hyperparameter tuning can vastly improve a model’s performance.
• Keep track of model metrics to understand how well it’s performing.