🔅 Python for Data Science and Machine Learning Essential Training Part 1

🌐 Author: Lillian Pierson, P.E.
🔰 Level: Intermediate

⏰ Duration: 7h 44m

🌀 Learn Python programming skills for data science and machine learning. Discover how to clean, transform, analyze, and visualize data, as you build a practical, real-world project.

📗 Topics: Data Science, Machine Learning, Python

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📱Python for Data Science and Machine Learning Essential Training Part 1

🔅 Python for Data Science and Machine Learning Essential Training Part 2

🌐 Author: Lillian Pierson, P.E.
🔰 Level: Intermediate

⏰ Duration: 5h 16m

🌀 In the second half of this two-part course, explore the essentials of using Python for data science and machine learning.

📗 Topics: Data Science, Machine Learning, Python

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📱Python for Data Science and Machine Learning Essential Training Part 2

🔗 Basics of Machine Learning 👇👇

Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:

1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.

2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.

3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.

📖 Key concepts include:

- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.

- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.

- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.

- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.

In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.