One Week of Data Science – New 2022!

What you’ll 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

DOWNLOAD LINK: One Week of Data Science – New 2022!