Empower your machine learning models with the best in data preprocessing and analysis techniques.

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.

Is This The Right Course?

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.

Who Should Attend?

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.

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We can host this training at your preferred location.

Prerequisites

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What You Will Learn

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.

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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
- 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.

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Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

Machine Learning Boot Camp Part 1: Data Prep Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.

Machine Learning Boot Camp Part 1: Data Prep Training in Norway

Is This The Right Course?

Who Should Attend?

Prerequisites

What You Will Learn

Outline

Avaible Training Dates