Introduction to Machine Learning

Introduction to Machine Learning, available at $54.99, has an average rating of 4, with 91 lectures, 1 quizzes, based on 2 reviews, and has 8 subscribers.

You will learn about Understand Machine Learning Fundamentals Preprocess Data: Perform data cleaning, handle missing values, and conduct feature engineering on datasets. Normalize and Standardize Data: Apply normalization and standardization techniques to prepare data for machine learning models. Explore Data Visually: Use data visualization techniques to understand data distributions and identify patterns. Perform Statistical Analysis: Conduct basic statistical analysis to summarize data characteristics. Analyze and preprocess the datasets to prepare it for machine learning modeling. Build a Simple Models: Construct a logistic regression model to predict outcomes. Evaluate Model Performance: Use accuracy, precision, recall, and F1-score to evaluate the performance of machine learning models. Split Data into Training and Testing Sets: Implement data splitting techniques to create training and testing datasets. Predict House Prices: Build and evaluate a regression model using the California housing dataset. Deploy a Machine Learning Model: Develop and deploy a machine learning model as a web application using Flask. This course is ideal for individuals who are Aspiring Data Scientists and Machine Learning Enthusiasts or Beginners with No Prior Experience or Programmers and software developers looking to expand their skill set by incorporating machine learning into their repertoire. or Data Analysts and Statisticians or Students and academics in fields related to computer science, mathematics, engineering, or data science. or Professionals from various industries (e.g., finance, healthcare, marketing) who want to understand how machine learning can be applied to their domain. or Individuals who enjoy learning new technologies and skills for personal enrichment. It is particularly useful for Aspiring Data Scientists and Machine Learning Enthusiasts or Beginners with No Prior Experience or Programmers and software developers looking to expand their skill set by incorporating machine learning into their repertoire. or Data Analysts and Statisticians or Students and academics in fields related to computer science, mathematics, engineering, or data science. or Professionals from various industries (e.g., finance, healthcare, marketing) who want to understand how machine learning can be applied to their domain. or Individuals who enjoy learning new technologies and skills for personal enrichment.

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Summary

Title: Introduction to Machine Learning

Price: $54.99

Average Rating: 4

Number of Lectures: 91

Number of Quizzes: 1

Number of Published Lectures: 91

Number of Published Quizzes: 1

Number of Curriculum Items: 92

Number of Published Curriculum Objects: 92

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

• Understand Machine Learning Fundamentals
• Preprocess Data: Perform data cleaning, handle missing values, and conduct feature engineering on datasets.
• Normalize and Standardize Data: Apply normalization and standardization techniques to prepare data for machine learning models.
• Explore Data Visually: Use data visualization techniques to understand data distributions and identify patterns.
• Perform Statistical Analysis: Conduct basic statistical analysis to summarize data characteristics.
• Analyze and preprocess the datasets to prepare it for machine learning modeling.
• Build a Simple Models: Construct a logistic regression model to predict outcomes.
• Evaluate Model Performance: Use accuracy, precision, recall, and F1-score to evaluate the performance of machine learning models.
• Split Data into Training and Testing Sets: Implement data splitting techniques to create training and testing datasets.
• Predict House Prices: Build and evaluate a regression model using the California housing dataset.
• Deploy a Machine Learning Model: Develop and deploy a machine learning model as a web application using Flask.

Who Should Attend

• Aspiring Data Scientists and Machine Learning Enthusiasts
• Beginners with No Prior Experience
• Programmers and software developers looking to expand their skill set by incorporating machine learning into their repertoire.
• Data Analysts and Statisticians
• Students and academics in fields related to computer science, mathematics, engineering, or data science.
• Professionals from various industries (e.g., finance, healthcare, marketing) who want to understand how machine learning can be applied to their domain.
• Individuals who enjoy learning new technologies and skills for personal enrichment.

Target Audiences

• Aspiring Data Scientists and Machine Learning Enthusiasts
• Beginners with No Prior Experience
• Programmers and software developers looking to expand their skill set by incorporating machine learning into their repertoire.
• Data Analysts and Statisticians
• Students and academics in fields related to computer science, mathematics, engineering, or data science.
• Professionals from various industries (e.g., finance, healthcare, marketing) who want to understand how machine learning can be applied to their domain.
• Individuals who enjoy learning new technologies and skills for personal enrichment.