Pythonn-machine-learning-with-python.ppt
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This document provides an overview of machine learning using Python, covering essential concepts such as packages, applications, benefits, and optimization techniques. It emphasizes the significance of libraries like TensorFlow, Scikit-learn, and PyTorch in developing machine learning models, as well as practical algorithms for classification, regression, and clustering. Additionally, it highlights the importance of structured code and community contributions for enhanced efficiency in machine learning projects.
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![Applications of Machine Learning Algorithms
• The developed machine learning algorithms are used in various
applications such as:
Web search
Computational biology
Finance
E-commerce
Space exploration
Robotics
Information extraction
Social networks
Debugging
Data mining
Expert systems
Robotics
Vision processing
Language processing
Forecasting things like
stock market trends,
weather
Pattern recognition
Games
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Pythonn-machine-learning-with-python.ppt
- 1. Machine Learning with Python Submitted to Dr. Ruchi Submitted by Kirti Saraf 10322210055 CSE(CORE)/A
- 2. Topics to be covered….. • Introduction to Machine Learning • Understanding of packages • Application of Machine Learning • Benefits of Machine Language • Commonly used Machine Learning • Python libraries for Machine Learning • Optimizing Machine Learning • Conclusion
- 3. Introduction to Machine Learning • Python is a popular platform used for research and development of production systems. It is a vast language with number of modules, packages and libraries that provides multiple ways of achieving a task. • Python and its libraries like NumPy, Pandas, SciPy, Scikit-Learn, Matplotlib are used in data science and data analysis. They are also extensively used for creating scalable machine learning algorithms.
- 4. • Python implements popular machine learning techniques such as Classification, Regression, Recommendation, and Clustering. • Python offers ready-made framework for performing data mining tasks on large volumes of data effectively in lesser time
- 5. Understanding the Need for Packages • Machine learning projects often involve intricate algorithms, data preprocessing, model training, and evaluation. • Packages provide a structured, reusable way to encapsulate these common tasks, saving developers time and effort. • They also promote code sharing, collaboration, and consistent best practices across an organization.
- 6. Applications of Machine Learning Algorithms • The developed machine learning algorithms are used in various applications such as: Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging Data mining Expert systems Robotics Vision processing Language processing Forecasting things like stock market trends, weather Pattern recognition Games [Your favorite area]
- 7. Benefits of Machine Learning • Powerful Processing • Better Decision Making & Prediction • Quicker Processing • Accurate • Affordable Data Management • Inexpensive • Analyzing Complex Big Data
- 8. Machine Learning Techniques Given below are some techniques in this Machine Learning tutorial. •Classification •Categorization •Clustering •Trend analysis •Anomaly detection •Visualization •Decision making
- 9. Representation • Decision trees • Sets of rules / Logic programs • Instances • Graphical models • Neural networks • Support vector machines (SVM) • Model ensembles etc………
- 10. Optimization • Combinatorial optimization – E.g.: Greedy search • Convex optimization – E.g.: Gradient descent • Constrained optimization – E.g.: Linear programming
- 11. Commonly Used Machine Language Packages TensorFlow A powerful open- source library for building and deploying ML models, especially for deep learning. Scikit-learn A comprehensive ML package with tools for classification, regression, clustering, and more. PyTorch A flexible deep learning framework known for its intuitive design and extensive community support.
- 12. Features of Machine Learning Let us look at some of the features of Machine Learning. •Machine Learning is computing-intensive and generally requires a large amount of training data. •It involves repetitive training to improve the learning and decision making of algorithms. •As more data gets added, Machine Learning training can be automated for learning new data patterns and adapting its algorithm.
- 13. Machine Learning Algorithms • Machine Learning can learn from labeled data (known as supervised learning) or unlabelled data (known as unsupervised learning). • Machine Learning algorithms involving unlabelled data, or unsupervised learning, are more complicated than those with the labeled data or supervised learning • Machine Learning algorithms can be used to make decisions in subjective areas as well.
- 14. Examples • Logistic Regression can be used to predict which party will win at the ballots. • Naïve Bayes algorithm can separate valid emails from spam. • Face detection: Identify faces in images (or indicate if a face is present). • Email filtering: Classify emails into spam and not- spam. • Medical diagnosis: Diagnose a patient as a sufferer or non-sufferer of some disease. • Weather prediction: Predict, for instance, whether or not it will rain tomorrow.
- 15. Libraries and Packages • To understand machine learning, you need to have basic knowledge of Python programming. In addition, there are a number of libraries and packages generally used in performing various machine learning tasks as listed below: – NumPy - is used for its N-dimensional array objects – pandas – is a data analysis library that includes data frames – matplotlib – is 2D plotting library for creating graphs and plots – scikit-learn - the algorithms used for data analysis and data mining tasks – seaborn – a data visualization library based on matplotlib
- 16. Importing and Utilizing Packages 1 Installation Packages are typically installed using package managers like pip or conda. 2 Importing Packages are imported using standard import statements at the beginning of your code. 3 Usage Access package functions and classes through dot notation, e.g. package.function().
- 17. Optimizing Package Performance Profiling Identify performance bottlenecks with profiling tools. Parallelization Leverage multiprocessing and GPU acceleration for faster computations. Caching Implement caching mechanisms to reuse intermediate results. Optimization Tune hyperparameters and model architectures for efficiency.
- 18. Developing Custom Packages Package Structure Organize your code into a hierarchical directory structure with an __init__.py file. Documentation Comprehensive docstrings, README files, and tutorials help users understand your package. Testing Implement unit tests to ensure your package's reliability and maintainability. Deployment Package your code and publish it to a repository like PyPI or Anaconda Cloud.
- 19. Conclusion and Best Practices Leverage Existing Packages Utilize well-maintained, community driven packages to accelerate development. Write Modular Code Design your own packages with a clear and extensible structure. Contribute to the Community Share your custom packages and insights to help others in the ML ecosystem. Stay Updated Regularly update your packages and dependencies to benefit from the latest improvements.