Machine Learning Boot Camp Part 1: Data Prep

In the world of machine learning, the quality of input data is critical. Machine learning models that use bad data input produce inaccurate and unreliable results, undermining their effectiveness and trustworthiness. Our Machine Learning Essentials Boot Camp: Preparing Your Data is a three-day hands-on skills immersion course geared for students who need to how to effectively prepare and optimize data for use in machine learning models, ensuring they produce accurate, useful and insightful predictions.

Throughout the course, guided by our expert instructor, you ll engage in workshop-style practical labs that will provide you with the real-world skills and hands-on experience needed to manage, prep and clean your data for successful machine learning model applications.

You ll learn how to translate diverse data into an analytically-friendly format, ensuring compatibility with machine learning algorithms. You ll learn how to scale and normalize data, ensuring consistent data representation, crucial for accurate model training and predictions. You'll navigate the intricacies of data transformation and refinement, and learn how to translate diverse datasets into formats friendly to machine learning algorithms. You ll also explore feature selection and dimensionality reduction, striking the balance between data richness and computational efficiency. You'll also grasp how to safeguard your data's journey with robust pipelines and preventive measures against data leakage, cementing the trustworthiness of your real-world model deployments. Lastly, you ll explore the complete lifecycle of a machine learning project, from data preparation to model deployment, you're equipped to oversee and implement comprehensive data-driven solutions.

By the end of this immersive boot camp, you ll be fully-equipped with a comprehensive skillset that not only enhances the predictive power of your models but also sets the foundation for innovative, data-driven solutions. You ll be ready to advance in your Machine Learning journey, leveraging your newly acquired skills towards model proficiency.

1. 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
2. Structural Analysis
   
   • Delve 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
3. 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
4. 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
5. 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.
6. Feature Selection
   
   • 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
7. 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
8. 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
9. 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
10. 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 projects.

This is an intermediate-level program, designed to prepare attendees for a deeper dive into next-level, heavy hands-on machine learning courses and workshops. Attendees should have practical, hands-on experience working with Python for Data Science, pandas and numpy.

This course is geared for data scientists and business professionals seeking to leverage data insights in decision-making. It's also ideal for software developers wanting to diversify their skills into the exciting field of machine learning.

Whether you're a student eager to jumpstart your career or an experienced professional looking to enhance your data-driven strategies, our hands-on workshop offers a valuable learning experience to transform you into a confident data handler and problem-solver.