{"id":1372,"date":"2019-02-15T10:32:06","date_gmt":"2019-02-15T10:32:06","guid":{"rendered":"http:\/\/kusuaks7\/?p=977"},"modified":"2023-07-27T15:58:26","modified_gmt":"2023-07-27T15:58:26","slug":"coding-deep-learning-for-beginners-linear-regression-part-1-initialization-and-prediction","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/coding-deep-learning-for-beginners-linear-regression-part-1-initialization-and-prediction\/","title":{"rendered":"Coding Deep Learning for Beginners\u200a\u2014\u200aLinear Regression (Part 1): Initializtion and Prediction"},"content":{"rendered":"

Ready to learn Machine Learning?Browse<\/em><\/strong> Machine Learning Training and Certification courses<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

This is the 3rd article of series \u201cCoding Deep Learning for Beginners<\/strong>\u201d. Here, you will find\u00a0links to the <\/em>1st article <\/a>and the\u00a02nd<\/sup> article<\/a>.<\/em><\/p><\/blockquote>\n

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Why Linear Regression?<\/strong><\/h3>\n

Some of you may wonder, why the article series about explaining and coding Neural Networks starts with\u00a0basic Machine Learning algorithm<\/strong>\u00a0such as Linear Regression. It\u2019s very justifiable to start from there. First of all, it is a very plain algorithm so the reader can grasp an\u00a0understanding of fundamental Machine Learning concepts<\/strong>\u00a0such as\u00a0Supervised Learning<\/em>,\u00a0Cost Function<\/em>, and\u00a0Gradient Descent<\/em>. Additionally, after learning Linear Regression it is quite easy to understand Logistic Regression algorithm and believe or not \u2014 it is possible to categorise that one as small Neural Network. You can expect all of those and even more covered in few next articles!<\/p>\n

Tools<\/strong><\/h3>\n

Let\u2019s introduce the\u00a0most popular libraries<\/strong>\u00a0that can be found in every Python based Machine Learning or Data Science related project.<\/p>\n