Python Machine Learning - Getting Started
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The document provides a comprehensive overview of getting started with machine learning in Python, covering essential resources, the importance of understanding data structures, and key definitions of machine learning. It highlights various machine learning algorithms such as regression, classification, clustering, and dimensionality reduction, along with foundational libraries like NumPy, pandas, and scikit-learn. Additionally, it discusses the interplay between machine learning and data science and emphasizes the significance of avoiding common pitfalls for beginners.
Python Machine Learning - Getting Started
- 1. Python Machine Learning Getting Started Rafey Iqbal Rahman GitHub: https://github.com/RafeyIqbalRahman LinkedIn: https://linkedin.com/in/rafeyirahman StackShare: https://stackshare.io/rafeyirahman
- 3. Resource List ● Machine Learning Mastery blog (Highly recommended) https://machinelearningmastery.com/blog/ ● Kaggle Notebooks https://www.kaggle.com/notebooks ● Kaggle Questions & Answers https://www.kaggle.com/questions-and-an swers ● Towards Data Science blog https://towardsdatascience.com/ ● Data Science, Machine Learning, Programming, Artificial Intelligence, and Technology topics of https://medium.com/. ● Analytics Vidhya blog https://www.analyticsvidhya.com/blog-arch ive/ ● Google Colaboratory https://colab.research.google.com
- 5. Don’ts of Getting Started
- 6. Here is what you should NOT do when you start studying machine learning in Python. 1. Get really good at Python programming and Python syntax. 2. Deeply understand the underlying theory and parameters for machine learning algorithms in scikit-learn*. 3. Avoid or lightly touch on all of the other tasks needed to complete a real project. - J. Brownlee in his book “Machine Learning Mastery with Python” * https://scikit-learn.org/stable/ Don’ts of Getting Started
- 7. But it’s important to know about data structures before getting started.
- 8. Data Structures in Python - An Overview ● list: sequence of mutable values ● tuple: sequence of immutable values ● dict: collection of key-value pairs
- 10. “Machine Learning is the interaction between theoretically sound computer science and practically noisy data. Essentially, it’s about machines making sense out of data in much the same way as humans do. Machine learning is a type of artificial intelligence whereby an algorithm or method extracts patterns from data.” - Matthew Kirk in his book “Thoughtful Machine Learning with Python” Machine Learning Definitions
- 11. “Machine Learning is the interaction between theoretically sound computer science and practically noisy data. Essentially, it’s about machines making sense out of data in much the same way as humans do. Machine learning is a type of artificial intelligence whereby an algorithm or method extracts patterns from data.” - Matthew Kirk in his book “Thoughtful Machine Learning with Python” Machine Learning Definitions
- 12. “ML is a multi-disciplinary approach, involving several scientific domains (e.g., mathematics, computer science, physics, biology, etc.), that enable computers to automatically learn from data. By learning we mean here a process that takes as input data and gives as output algorithms capable of performing, over the same kind of data, a desired task.” - From the book “Machine Learning for Audio, Image and Video Analysis, 2e” Machine Learning Definitions
- 13. “You will find it difficult to describe your mother’s face accurately enough for your friend to recognize her in a supermarket. But if you show him a few of her photos, he will immediately spot the tell-tale traits he needs. As they say, a picture- an example -is worth a thousand words. This is what we want our technology to emulate. Unable to define certain objects or concepts with accuracy, we want to convey them to the machine by ways of examples. For this to work, however, the computer has to be able to convert the examples into knowledge. Hence our interest in algorithms and techniques for machine learning …” - From the book “An Introduction to Machine Learning, 2e” Machine Learning Definitions
- 14. “Machine learning is functionality that helps software perform a task without explicit programming or rules.” - Google Machine Learning Definitions
- 15. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 16. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 17. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 18. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 19. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 20. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 21. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 22. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 23. - Image by “Amazon Machine Learning on SlideShare” How Explicit Programming Looks Like?
- 24. - Image by “Amazon Machine Learning on SlideShare” Machine Learning - Ditch Explicit Programming
- 25. Applications of Machine Learning
- 26. - Image by “Amazon Machine Learning on SlideShare” Applications of Machine Learning
- 27. - Image by “Amazon Machine Learning on SlideShare” Applications of Machine Learning
- 29. “Facebook asks you to list your hometown and your current location, ostensibly to make it easier for your friends to find and connect with you. But it also analyzes these locations to identify global migration patterns and where the fanbases of different football teams live.” - From the book “Data Science from Scratch” Understanding Data Science
- 30. Machine Learning and Data Science go hand-in-hand.
- 31. Getting Started
- 32. Getting Started with ML Algorithms ● Regression: It is used to predict continuous variables (for e.g., salary, weight, temperature, etc.). It is a form of supervised learning since the input is mapped to an output using input-output pairs. Popular regression algorithms include Linear, eXtreme Gradient Boosting (XGBoost), CatBoost, LightGBM, Lasso, Decision Tree, Random Forest, and Quantile regression. ● Classification: It is used to categorize data points (for e.g., spam filtering, predicting survival of people due to a collapsed building, etc.). It is a form of supervised learning. Popular classification algorithms include Logistic regression (yes, I said what I said), Support Vector Machines (SVM), Naive Bayes, XGB, and KNN (K Nearest Neighbor) classifier.
- 33. Getting Started with ML Algorithms ● Clustering: It involves grouping data points into ‘clusters’ on the basis of their similarity (for e.g., grouping customers into clusters on the basis of their consumer behavior). It is a form of unsupervised learning since the structure from the given input is not already defined. Unsupervised learning involves finding hidden structures in the data. Popular clustering algorithm K-Means, Hierarchical, DBSCAN clustering, and Gaussian Mixture Model (GMM). ● Dimensionality Reduction: As the name suggests, it reduces the number of input dimensions (variables). It is also used to conceal confidential data (for e.g., banking transactions, etc.). It is a form of unsupervised learning. Popular dimensionality reduction algorithms include t-SNE (t-distributed Stochastic Neighbor Embedding), and PCA (Principal Component Analysis).
- 34. - Images by “SAS Blogs” Regression Classification
- 35. - Images by “SAS Blogs” Clustering Dimensionality Reduction
- 36. Getting Started with Core Libraries ● NumPy: NumPy (Numerical Python) is used to perform linear algebraic operations (for e.g., matrix operations). In Numpy, matrices are referred to as ‘arrays’. ● Pandas: Pandas is useful for performing analysis of the data. It can also perform basic visualization of the data (for e.g., line, scatter, box, bar plots, etc.) ● Matplotlib: The most comprehensive data visualization library Python ever had. Almost any graph can be plotted using the Matplotlib library. However, the resulting graphs are bland in nature by default and therefore require additional customization. ● Seaborn: Seaborn is a data visualization library and is one step ahead of Matplotlib. It requires less code to plot some graphs and produces more visually-appealing graphs.
- 37. Getting Started with Core Libraries ● Plotly: The plots produced by Matplotlib and Seaborn are non-interactive, while Plotly yields interactive plots. The interactivity is done via hovering over the plot. ● Scikit-Learn: Often referred to as the Swiss Army knife of machine learning, Scikit-Learn (sklearn) can perform a multitude of ML operations on the data, including data scaling, clustering, dimensionality reduction, and encoding (converting categorical variables into numeric variables). ● Scipy: The go-to library for performing statistical operations on the data (for e.g., calculating the skewness and kurtosis, performing probability tests, hypothesis testing, etc.). ● OpenCV: OpenCV (opencv-python) is an intuitive library for performing image processing. Pose estimation, face mask detection, etc. are possible through this library.