Real Life Machine Learning and Data Science Projects

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Best Real Life Data Science and Machine Learning Project for Beginners Let’s start with the Definition of basic keywords like Machine Learning, Data Science, NLP, and Deep Learning. What is Machine Learning?Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. What is Data Science?Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains. What is NLP?Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. What is Deep Learning?Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. What is Artificial Intelligence?Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals and humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals Now let’s see what you are going to Learn in this Course: Learn to Upload Dataset in the Google Colab and find the path of the dataset Learn to read the dataset using Pandas Learn to use Google Colab for Machine Learning and Data Science Projects Learn to Handling Missing Values in the Real World Dataset for Categorical and Numerical Features Learn to perform Label Encoding Learn to Perform Splitting the Dataset in Training and Testing Learn to Model Formation using KNN, Logistic Regression, SVM, XGBoost Regressor Learn about Data Visualization using Seaborn and Matplotlib Learn to Build your own Predictive System Learn about Developing an intuition to solve Machine Learning and Data Science problem in Real World Learn to do Model Evaluation using R Squared and Accuracy Score Now the next things that come to the mind is Who is this course for?This course is for:- Beginner Python Developer curious to develop problem-solving in Data Science and Machine Learning ML experts, Data Scientist and Data Analyst who wants some super quick brush up Anyone who is keen to know about Machine Learning and Data Science in a Project-Based Approach A positive mindset for SUCCESSThis mindblowing course will instil in you the confidence to move ahead in the world of Machine Learning and Data Science and make you a competitive individual to take the Booming Job Opportunity. I welcome you All in this course and wish you Happy Learning!!!