Machine Learning Basics For All - W.I.P

Using This Repository

Clone and run the repository with:


git clone https://github.com/engagepy/machine-learning-basics.git
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
python3 <filename>.py

Lessons

Each file is a lesson covering a specific ML topic:

data_centering.py - Data Centering in Machine Learning

🔑 >centered_data = data - mean

data_standardization.py - Standardization Foundation Math

🔑 standardized_data = (data - mean)/std_dev

standard_scaler.py - Standard Scaler via Scikit-Learn

🔑 sklearn.preprocessing import StandardScaler

min_max_normalization.py - Min-Max Normalization Foundation Math

🔑 spent_normalized = (spent - min_spent)/(max_spent - min_spent)

min_max_scaler.py - Min-Max Scaler via Scikit-Learn

🔑 from sklearn.preprocessing import MinMaxScaler

binning.py - Binning in Machine Learning via Scikit-Learn

🔑 pd.cut(ages, age_bins, right=False)

Introduction To Machine Learning Basics

Machine Learning (ML) is a subset of artificial intelligence focused on developing systems that learn and improve from experience without being explicitly programmed. It's similar to data mining in that both search for patterns in data, but ML uses that data to adjust program actions automatically.

Types of Machine Learning

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Supervised Learning

Trained using labeled datasets. The algorithm learns the relationship between input and output.

Unsupervised Learning

Works with unlabeled data to find hidden patterns or intrinsic structures.

Reinforcement Learning

Agents learn by interacting with their environment and receiving rewards for positive outcomes.

Machine Learning Algorithms

Supervised Learning Algorithms

Linear Regression
Logistic Regression
Decision Trees
Random Forest
Gradient Boosting
Support Vector Machines
Neural Networks

Unsupervised Learning Algorithms

K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Independent Component Analysis
Singular Value Decomposition
Apriori Algorithm

Reinforcement Learning Algorithms

Q-Learning
SARSA
Deep Adversarial Networks
Deep Deterministic Policy Gradient
Asynchronous Advantage Actor-Critic

Conclusion

Machine learning powers various applications, from recommendation systems to self-driving cars. Understanding the different types of learning models and algorithms is crucial in leveraging ML effectively. This repository aims to provide a comprehensive introduction to machine learning basics for beginners.