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    The Basics of Machine Learning With Python You Need To Know


    Table of Contents:

      1. Introduction to Machine Learning with Python
      2. Why is Python Used in Machine Learning?
      3. Machine Learning Algorithms in Python
      4. Conclusion

    Simplifying Technical Jargon

    Technical jargons can be overwhelming. Let us simplify some of them.
    Python is a programming language.
    Data science encompasses the entire process of data analysis, from collecting, cleaning and transforming data, to using statistical models to uncover patterns and make predictions.

    Machine learning, and deep learning are branches of artificial intelligence that deal with analyzing, processing and deriving insights from data. They are widely applied across various industries to solve problems and make predictions.
    Machine learning involves training algorithms with large amounts of data and using the resulting models to make predictions on new data. It includes supervised, unsupervised, and reinforcement learning techniques.

    Why is Python Used in Machine Learning?

    Python is widely used in the field of machine learning due to several advantages. Firstly, it boasts of platform independence, allowing for seamless execution across different operating systems without the need for code modifications. This results in a more efficient distribution of software, as well as reduced development time and resources.

    Additionally, the language itself is known for its simplicity and consistency, making it a favored choice among developers. Its concise and readable code enhances the presentation process
    and enables easier collaboration with other developers. The ease of its usage also makes it a suitable option for beginners, while still providing experienced developers with the stability and reliability they need to create innovative solutions.

    Furthermore, Python is equipped with an abundance of robust open-source libraries available, such as scikit-learn, TensorFlow, and PyTorch. These libraries offer a wealth of pre-implemented algorithms and tools, making it easier for developers to construct sophisticated machine learning models and experiment with different techniques.

    Deep learning is a subset of machine learning that involves training artificial neural networks to recognize patterns in large amounts of data.

    Python with its libraries like TensorFlow, Keras, and PyTorch, can be used by developers to build complex deep learning models for tasks such as image and speech recognition, natural language processing, and much more. These models can then be fine-tuned and improved through training on vast amounts of data, allowing for highly accurate predictions and classifications.
    Also Read: Every little detail on the Fundamentals of Computer Programming

    Machine Learning Algorithms in Python

    Let us look at some of the machine learning algorithms in Python that can be implemented using the libraries we mentioned above:

    Linear Regression :

    This is a simple regression algorithm that models the relationship between a dependent variable and one or more independent variables as a linear equation.

    Logistic Regression :

    It is a classification algorithm used to predict a binary outcome (yes/no, true/false) based on one or more independent variables.

    Decision Trees :

    This is a tree-based algorithm where each node represents a decision and each leaf node represents an outcome. It is used for both regression and classification problems.

    Random Forest :

    This is an ensemble learning method for both classification and regression, where multiple decision trees are combined to produce a more accurate prediction.

    SVM (Support Vector Machines) :

    This is a supervised machine learning algorithm used for classification and regression. It seeks to find the best boundary between classes by maximizing the margin between them.

    K-Nearest Neighbors (KNN) :

    This is a simple algorithm that classifies data points based on the majority vote of its k nearest neighbors. It is used for both regression and classification problems.

    Naive Bayes :

    It is a probabilistic algorithm based on Bayes’ theorem that makes classifications based on the probability of the data points belonging to a certain class.

    K-Means Clustering :

    This is an unsupervised learning algorithm that groups similar data points together into clusters based on their proximity to each other. It is used for clustering and dimensionality reduction.

    Some Concluding thoughts

    The promise and potential of machine learning in solving the biggest challenges in our world were seen in how organisations responded to the global fight against COVID-19. They have efficiently applied their machine learning expertise in several areas: enhancing customer communications, understanding how COVID-19 spreads and accelerating research and treatment.

    It is an understatement to reiterate that AI and Machine Learning is the need of the hour. Recognising its importance, the Government of India announced in the recent 2023 Budget that they will be setting up three centers of excellence in artificial intelligence in the country, skilling in areas such as machine learning and robotics, and the launch of the national data governance framework. It goes without saying that Atria University is committed to this vision of empowering the youth to advance India’s AI leadership for solving both global and grassroots problems though industry aligned majors in Interactive Technologies and Digital Transformation


    Atria Admissions Team

    March 6, 2023

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