One Week of Data Science in Python – New 2024!

One Week of Data Science in Python – New 2024!, available at $94.99, has an average rating of 4.39, with 145 lectures, based on 283 reviews, and has 3778 subscribers.

You will learn about Perform statistical analysis on real world datasets Understand feature engineering strategies and tools Perform one hot encoding and normalization Understand the difference between normalization and standardization Deal with missing data using pandas Change pandas DataFrame datatypes Define a function and apply it to a Pandas DataFrame column Perform Pandas operations and filtering Calculate and display correlation matrix heatmap Perform data visualization using Seaborn and Matplotlib libraries Plot single line plot, pie charts and multiple subplots using matplotlib Plot pairplot, countplot, and correlation heatmaps using Seaborn Plot distribution plot (distplot), Histograms and scatterplots Understand machine learning regression fundamentals Learn how to optimize model parameters using least sum of squares Split the data into training and testing using SK Learn Library Perform data visualization and basic exploratory data analysis Build, train and test our first regression model in Scikit-Learn Assess trained machine learning regression model performance Understand the theory and intuition behind boosting Train an XG-boost algorithm in Scikit-Learn to solve regression type problems Train several machine learning models classifier models such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC. Compare the performance of the classification model using various KPIs. Apply autogluon to solve regression and classification type problems Use AutoGluon library to perform prototyping of AI/ML models using few lines of code Plot various models’ performance on model leaderboard Optimize regression and classification models hyperparameters using SK-Learn Learn the difference between various hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization. Perform hyperparameters optimization using Scikit-Learn library. Understand bias variance trade-off and L1 and L2 regularization This course is ideal for individuals who are The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problems or Beginners Data Scientists wanting to advance their careers and build their portfolio or Seasoned consultants wanting to transform businesses by leveraging Data Science or Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience It is particularly useful for The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problems or Beginners Data Scientists wanting to advance their careers and build their portfolio or Seasoned consultants wanting to transform businesses by leveraging Data Science or Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience.

Enroll now: One Week of Data Science in Python – New 2024!

Summary

Title: One Week of Data Science in Python – New 2024!

Price: $94.99

Average Rating: 4.39

Number of Lectures: 145

Number of Published Lectures: 136

Number of Curriculum Items: 145

Number of Published Curriculum Objects: 136

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

• Perform statistical analysis on real world datasets
• Understand feature engineering strategies and tools
• Perform one hot encoding and normalization
• Understand the difference between normalization and standardization
• Deal with missing data using pandas
• Change pandas DataFrame datatypes
• Define a function and apply it to a Pandas DataFrame column
• Perform Pandas operations and filtering
• Calculate and display correlation matrix heatmap
• Perform data visualization using Seaborn and Matplotlib libraries
• Plot single line plot, pie charts and multiple subplots using matplotlib
• Plot pairplot, countplot, and correlation heatmaps using Seaborn
• Plot distribution plot (distplot), Histograms and scatterplots
• Understand machine learning regression fundamentals
• Learn how to optimize model parameters using least sum of squares
• Split the data into training and testing using SK Learn Library
• Perform data visualization and basic exploratory data analysis
• Build, train and test our first regression model in Scikit-Learn
• Assess trained machine learning regression model performance
• Understand the theory and intuition behind boosting
• Train an XG-boost algorithm in Scikit-Learn to solve regression type problems
• Train several machine learning models classifier models such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier
• Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC.
• Compare the performance of the classification model using various KPIs.
• Apply autogluon to solve regression and classification type problems
• Use AutoGluon library to perform prototyping of AI/ML models using few lines of code
• Plot various models’ performance on model leaderboard
• Optimize regression and classification models hyperparameters using SK-Learn
• Learn the difference between various hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization.
• Perform hyperparameters optimization using Scikit-Learn library.
• Understand bias variance trade-off and L1 and L2 regularization

Who Should Attend

• The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problems
• Beginners Data Scientists wanting to advance their careers and build their portfolio
• Seasoned consultants wanting to transform businesses by leveraging Data Science
• Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience

Target Audiences

• The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problems
• Beginners Data Scientists wanting to advance their careers and build their portfolio
• Seasoned consultants wanting to transform businesses by leveraging Data Science
• Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience