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Machine Learning Bootcamp – Part 1: Preparing Your Data
Description
Machine Learning Bootcamp – Part 1: Preparing Your Data Introduction
Machine Learning Bootcamp – Part 1: Course Outline
Getting Started with Data
- Explore the role and importance of data in machine learning.
- Encoding data: Transform raw data into a format suitable for analytics.
- Dealing with the curse of dimensionality: Navigate high-dimensional spaces effectively.
- Scaling and normalizing data: Standardize data for consistent analysis.
- Hands-on Activity / Lab
Structural Analysis
- Dive into the intricate patterns that define data.
- Importing libraries: Equip yourself with the right tools for data manipulation.
- Importing data: Initiate the first steps of data-driven exploration.
- Conducting basic data investigation: Peek into the essence of your dataset.
- Utilizing relevant tools for data structure analysis: Get acquainted with state-of-the-art tools to dissect data structure.
- Hands-on Activity / Lab
Quality Analysis
- Refine data sets by spotting and fixing errors.
- Identifying and removing duplicates: Ensure uniqueness in your dataset.
- Handling null values and missing data: Fill the gaps in your data with precision.
- Detecting and managing outliers: Understand and manage extreme data points.
- Working with dates in data: Harness the power of time-series data.
- Hands-on Activity / Lab
Exploratory Data Analysis
- Dive deep into data to extract meaningful insights.
- Conducting univariate analysis: Analyze one variable at a time.
- Conducting bivariate analysis: Discover relationships between two variables.
- Conducting multivariate analysis: Understand complex data interactions.
- Using pivot tables for data analysis: Summarize data visually and numerically.
- Understanding correlation: Measure linear relationships between variables.
- Understanding mutual information: Gauge dependency between variables.
- Hands-on Activity / Lab
Data Features
- Pinpoint the most impactful data components.
- Identifying and dropping unused columns: Streamline data for efficiency.
- Detecting and handling low variance or no variance columns: Maintain data variability.
- Understanding multicollinearity (VIF): Ensure independent predictor variables.
Selection of Features
- Prioritize the most relevant data features for robust models.
- Using wrappers (RFE, Forward, Backward selection): Implement dynamic feature selection.
- Using filters (Statistical tests): Opt for features based on statistical relevance.
- Using embedded methods: Integrate feature selection into algorithm functionality.
- Understanding unsupervised feature selection methods: Navigate feature selection without target variables.
- Hands-on Activity / Lab
Feature Importance
- Gauge the significance of different data features in prediction.
- Understanding dimensionality reduction: Simplify data without losing information.
- Using Principal Component Analysis (PCA): Transform data to highlight variance.
- Using Linear Discriminant Analysis (LDA): Optimize class separability.
- Hands-on Activity / Lab
Encoding, Scaling, and Skewness
- Tailor data formats for better compatibility with machine learning algorithms.
- Encoding categorical variables: Convert categories into numerical values.
- Scaling numerical variables: Maintain consistency in data magnitude.
- Detecting and correcting skewness in data: Normalize data distributions.
- Hands-on Activity / Lab
Pipelines
- Streamline machine learning workflows with seamless data transitions.
- Understanding the role of pipelines in machine learning: Appreciate the significance of efficient workflows.
- Creating and implementing data preprocessing pipelines: Process data in a structured manner.
- Using pipelines for efficient cross-validation and hyperparameter tuning: Optimize model parameters with ease.
- Hands-on Activity / Lab
Introduction to Machine Learning
- Lay the groundwork for next-level machine learning practices.
- Understanding k-fold cross-validation: Assess model performance effectively.
- Using resampling techniques: Balance dataset disparities.
- Dividing data into training and test sets: Create a structured environment for model training and evaluation.
- Identifying and preventing data leakage: Maintain the integrity of your datasets.
- Understanding the basic types and applications of machine learning models
- Capstone Project: Develop an end-to-end machine learning model: Apply the course skills to develop a complete data-driven project.
$2295.00
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3 Days Course |