Python for Machine Learning

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Machine Learning. With a focus on SciKit Learn, you’ll learn all aspects of Machine Learning ranging from a variety of regression types (Linear / Lasso /Ridge), Elastic Net, K Nearest Neighbors and Means Clustering, Hierarchal Clustering, DBSCAN, PCA, and Model Deployment.

Machine Learning concepts

• Supervised vs Unsupervised Learning
  • Supervised learning: This type of machine learning uses labeled data to train a model. The model learns to predict the output for a given input.
  • Unsupervised learning: This type of machine learning uses unlabeled data to train a model. The model learns to find patterns in the data.
• Types of Machine Learning – Classification vs Regression
  • Classification: This type of machine learning predicts a categorical output. For example, a classification model could predict whether a customer will churn or not.
  • Regression: This type of machine learning predicts a continuous output. For example, a regression model could predict the price of a house.
• Evaluation
  • There are many different metrics for evaluating machine learning models. Some common metrics include accuracy, precision, recall, and F1 score.

Machine Learning Methods – All in Theory and Practice

• Linear Regression
  • Linear regression is a simple but powerful machine learning method. It can be used to predict a continuous output.
  • Libraries: NumPy, SciPy, Scikit-learn
  • Use cases: Predicting house prices, predicting customer churn, predicting sales
• Logistic Regression
  • Logistic regression is a type of classification machine learning method. It can be used to predict a binary output.
  • Libraries: NumPy, SciPy, Scikit-learn
  • Use cases: Predicting whether a customer will churn, predicting whether a patient has cancer
• K Nearest Neighbors
  • K nearest neighbors is a simple but effective machine learning method. It predicts the output for a given input by finding the k most similar inputs and averaging their outputs.
  • Libraries: Scikit-learn
  • Use cases: Predicting customer behavior, recommending products, classifying images
• Support Vector Machine
  • Support vector machine is a powerful machine learning method. It can be used for both classification and regression tasks.
  • Libraries: Scikit-learn
  • Use cases: Classifying images, predicting customer churn, predicting sales
• Decision Trees
  • Decision trees are a simple but effective machine learning method. They predict the output for a given input by following a series of if-then-else rules.
  • Libraries: Scikit-learn
  • Use cases: Predicting customer behavior, recommending products, classifying images

Unsupervised Learning Methods

• Clustering
  • Clustering is a type of unsupervised machine learning method. It groups similar data points together.
  • Libraries: Scikit-learn
  • Use cases: Customer segmentation, market basket analysis, image clustering
• Anomaly Detection
  • Anomaly detection is a type of unsupervised machine learning method. It identifies data points that are significantly different from the rest of the data.
  • Libraries: Scikit-learn
  • Use cases: Fraud detection, intrusion detection, quality control

Feature Engineering and Data Preparation

• Feature engineering is the process of transforming raw data into features that are useful for machine learning models.
• Data preparation is the process of cleaning and formatting data so that it can be used by machine learning models.
• Libraries: NumPy, SciPy, Pandas

Experienced Python developers looking to understand a wide variety of machine learning algorithms, including supervised and unsupervised learning algorithms.