- Machine Learning with Python
- Home
- Basics
- Python Ecosystem
- Methods for Machine Learning
- Data Loading for ML Projects
- Understanding Data with Statistics
- Understanding Data with Visualization
- Preparing Data
- Data Feature Selection
- ML Algorithms − Classification
- Introduction
- Logistic Regression
- Support Vector Machine(SVM)
- Decision Tree
- Naïve Bayes
- Random Forest
- ML Algorithms − Regression
- Overview
- Linear Regression
- ML Algorithms − Clustering
- Overview
- K-Means Algorithm
- Mean Shift Algorithm
- Hierarchical Clustering
- ML Algorithms − KNN Algorithm
- Finding Nearest Neighbors
- Performance Metrics
- Automatic Workflows
- Improving Performance of ML Models
- Improving Performance of ML Model(contd..)

- Useful Resources
- Quick Guide
- Useful Resources
- Discussion

These methods are different from previously studied methods and very rarely used also. In this kind of learning algorithms, there would be an agent that we want to train over a period of time so that it can interact with a specific environment. The agent will follow a set of strategies for interacting with the environment and then after observing the environment it will take actions regards the current state of the environment. The following are the main steps of reinforcement learning methods.

**Step 1**− First, we need to prepare an agent with some initial set of strategies.**Step 2**− Then observe the environment and its current state.**Step 3**− Next, select the optimal policy regards the current state of the environment and perform important action.**Step 4**− Now, the agent can get corresponding reward or penalty as per accordance with the action taken by it in previous step.**Step 5**− Now, we can update the strategies if it is required so.**Step 6**− At last, repeat steps 2-5 until the agent got to learn and adopt the optimal policies.

machine_learning_with_python_methods.htm

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