Data Science , Machine Learning Concepts | Data Science , Machine Learning : Ultimate Course For All
Description
Data Science , Machine Learning : Ultimate Course For All
Course Description:
Welcome to the ultimate Data Science , Machine Learning course for 2025 – your complete guide to mastering Data Science , Machine Learning from the ground up with real-world examples and hands-on projects.
This course is designed for beginners and intermediate learners who want to dive deep into the fields of Data Science , Machine Learning. Whether you’re starting from zero or brushing up your skills, this course will walk you through all the essential concepts, tools, and techniques used in Data Science , Machine Learning today.
You’ll begin by understanding the core principles of Data Science , Machine Learning, then move into Python programming, data preprocessing, model training, evaluation, and deployment. With step-by-step explanations and practical exercises, you’ll gain real-world experience in solving problems using Data Science , Machine Learning.
By the end of the course, you’ll be fully equipped to handle real projects and pursue career opportunities in Data Science , Machine Learning confidently.
Class Overview:
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Introduction to Data Science , Machine Learning:
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Understand the principles and concepts of data science and machine learning.
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Explore real-world applications and use cases of data science across various industries.
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Python Fundamentals for Data Science:
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Learn the basics of Python programming language and its libraries for data science, including NumPy, Pandas, and Matplotlib.
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Master data manipulation, analysis, and visualization techniques using Python.
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Data Preprocessing and Cleaning:
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Understand the importance of data preprocessing and cleaning in the data science workflow.
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Learn techniques for handling missing data, outliers, and inconsistencies in datasets.
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Exploratory Data Analysis (EDA):
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Perform exploratory data analysis to gain insights into the underlying patterns and relationships in the data.
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Visualize data distributions, correlations, and trends using statistical methods and visualization tools.
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Feature Engineering and Selection:
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Engineer new features and transform existing ones to improve model performance.
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Select relevant features using techniques such as feature importance ranking and dimensionality reduction.
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Model Building and Evaluation:
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Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and gradient boosting.
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Evaluate model performance using appropriate metrics and techniques, including cross-validation and hyperparameter tuning.
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Advanced Machine Learning Techniques:
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Dive into advanced machine learning techniques such as support vector machines (SVM), neural networks, and ensemble methods.
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Model Deployment and Productionization:
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Deploy trained machine learning models into production environments using containerization and cloud services.
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Monitor model performance, scalability, and reliability in production and make necessary adjustments.
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Enroll now and unlock the full potential of data science and machine learning with the Complete Data Science and Machine Learning Course!
Total Students | 11645 |
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Original Price($) | |
Sale Price | Free |
Number of lectures | 65 |
Number of quizzes | 1 |
Total Reviews | 168 |
Global Rating | 4.36 |
Instructor Name | ARUNNACHALAM SHANMUGARAAJAN |
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