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
