Throughout the course you will explore:  

• Data Encoding: Dive into data encoding to seamlessly translate diverse information into a machine-friendly format. 
• Data Manipulation Mastery: You'll get comfortable with encoding, scaling, and normalizing data. By the end of the course, the curse of dimensionality will no longer be a challenge. 
• Quality Analysis Confidence: Learn how to identify and remove duplicates, handle null values, manage outliers, and work with dates in your data. You'll be a pro at maintaining clean datasets. 
• Feature Analysis Wizardry: Discover how to identify unused columns, detect low variance ones, and understand multicollinearity. By the end of the workshop, feature selection will feel like second nature. 
• Pipeline Proficiency: Gain a deep understanding of the critical role of pipelines in machine learning and develop the skills to create and implement your own data preprocessing pipelines. 
• Machine Learning Basics: Get introduced to the fundamentals of machine learning, understand k-fold cross-validation, master the art of partitioning data, and learn how to prevent data leakage. You'll be set to step confidently into the world of machine learning. 

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. 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We'll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation. 

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 

• 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 

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. 

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 

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 projects.