Machine Learning Engineering Tools for Beginners

Machine Learning Engineering Tools for Beginners, available at $54.99, has an average rating of 5, with 74 lectures, based on 3 reviews, and has 1013 subscribers.

You will learn about An understanding of the fundamental principles of machine learning. The differences between various types of machine learning: Supervised, Unsupervised, and Reinforcement Learning. Real-world applications of machine learning across different industries. Basics of Python programming, including data types, variables, and operators. How to work with Jupyter Notebooks for Python coding and data analysis. The usage of key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. Different types of data: structured and unstructured data. Techniques for data preprocessing: cleaning, transformation, and normalization. How to conduct feature extraction and selection. Understanding and applying descriptive statistics in data analysis. Data visualization techniques using Matplotlib and Seaborn. The concepts of correlation and covariance in data. Implementing basic machine learning algorithms like Linear Regression and Logistic Regression Introduction to classification techniques: Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). Unsupervised learning techniques like K-Means and Hierarchical Clustering. The concepts of overfitting, underfitting and understanding the bias-variance trade-off. Evaluation metrics for regression and classification tasks. Techniques for model validation, including cross-validation. An introduction to deep learning and neural networks. The architecture and applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). How to use Scikit-Learn for building and training models. Techniques for hyperparameter tuning and model optimization. An introduction to Natural Language Processing (NLP). Text cleaning and preprocessing techniques for NLP. An overview of basic NLP algorithms. Understanding the concept of bias in machine learning models. Learning about the ethical implications of machine learning. Strategies for reducing bias and promoting fairness in machine learning models. Hands-on experience applying machine learning techniques to real-world datasets. Steps for continuing learning and advancing in the field of Machine Learning Engineering. This course is ideal for individuals who are Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals. or Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain. or Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning. or Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets. or Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains. or AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI. or The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning. It is particularly useful for Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals. or Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain. or Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning. or Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets. or Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains. or AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI. or The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning.

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Summary

Title: Machine Learning Engineering Tools for Beginners

Price: $54.99

Average Rating: 5

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $64.99

Quality Status: approved

Status: Live

What You Will Learn

• An understanding of the fundamental principles of machine learning.
• The differences between various types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
• Real-world applications of machine learning across different industries.
• Basics of Python programming, including data types, variables, and operators.
• How to work with Jupyter Notebooks for Python coding and data analysis.
• The usage of key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.
• Different types of data: structured and unstructured data.
• Techniques for data preprocessing: cleaning, transformation, and normalization.
• How to conduct feature extraction and selection.
• Understanding and applying descriptive statistics in data analysis.
• Data visualization techniques using Matplotlib and Seaborn.
• The concepts of correlation and covariance in data.
• Implementing basic machine learning algorithms like Linear Regression and Logistic Regression
• Introduction to classification techniques: Decision Trees, Random Forests, and K-Nearest Neighbors (KNN).
• Unsupervised learning techniques like K-Means and Hierarchical Clustering.
• The concepts of overfitting, underfitting and understanding the bias-variance trade-off.
• Evaluation metrics for regression and classification tasks.
• Techniques for model validation, including cross-validation.
• An introduction to deep learning and neural networks.
• The architecture and applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
• How to use Scikit-Learn for building and training models.
• Techniques for hyperparameter tuning and model optimization.
• An introduction to Natural Language Processing (NLP).
• Text cleaning and preprocessing techniques for NLP.
• An overview of basic NLP algorithms.
• Understanding the concept of bias in machine learning models.
• Learning about the ethical implications of machine learning.
• Strategies for reducing bias and promoting fairness in machine learning models.
• Hands-on experience applying machine learning techniques to real-world datasets.
• Steps for continuing learning and advancing in the field of Machine Learning Engineering.

Who Should Attend

• Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals.
• Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain.
• Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning.
• Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets.
• Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains.
• AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI.
• The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning.

Target Audiences

• Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals.
• Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain.
• Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning.
• Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets.
• Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains.
• AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI.
• The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning.