Description
What you’ll learn
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Master the essential concepts, techniques, and tools of data science and machine learning.
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Acquire hands-on experience with Python programming and its libraries for data manipulation, analysis, and visualization.
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Build and evaluate predictive models using a variety of machine learning algorithms and techniques.
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Complete Data Science & Machine Learning Course
Course Title: Complete Data Science and Machine Learning Course
Course Description:
Welcome to the “Complete Data Science and Machine Learning Course”! In this comprehensive course, you will embark on a journey to master the fundamentals of data science and machine learning, from data preprocessing and exploratory data analysis to building predictive models and deploying them into production. Whether you’re a beginner or an experienced professional, this course will provide you with the knowledge and skills needed to succeed in the dynamic field of data science and machine learning.
Class Overview:
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Introduction to Data Science and 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!
Who this course is for:
- Students and professionals interested in pursuing a career in data science, machine learning, or artificial intelligence.
- Professionals seeking to enhance their skills and stay competitive in the rapidly evolving field of data science and machine learning.
Course content
- Introduction To Complete Data Science & Machine Learning Course1 lecture • 1min
- Introduction To Complete Data Science & Machine Learning Course
- Complete Python Programming Course30 lectures • 2hr 4min
- Complete Python Programming Course
- Complete Data Science Course25 lectures • 1hr 1min
- Complete Data Science Course
- Complete Machine Learning Course9 lectures • 1hr 5min
- Complete Machine Learning Course
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