One Week of Data Science in Python – New 2023!
Master Data Science Fundamentals Quickly & Efficiently in one week! Course is Designed for Busy People
Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team | 13 hours on-demand video course
Do you want to Learn Data Science and build powerful applications Quickly and Efficiently? Are you an absolute beginner who want to break into Data Science and looking for a course that includes all the basics you need? Are you a busy aspiring entrepreneur who wants to maximize business revenues and reduce costs with Data Science but don’t have the time to get there quickly and efficiently? If the answer is yes to any of these questions, then this course is for you!
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