Building Intelligent Applications with AI and ML - Level 1 Training in Finland

  • Learn via: Classroom
  • Duration: 3 Days
  • Price: From €2,853+VAT
We can host this training at your preferred location. Contact us!

Exclusive - Learn the fundamentals of Artificial Intelligence and Machine Learning to develop intelligent applications.

Intelligent applications that are developed using Artificial Intelligence and Machine Learning are helping businesses take critical initiatives. These applications incorporate the power of predictive and prescriptive analytics, consumer data, and cutting-edge technologies. They are used by many organizations and enterprises to automatically discover, learn and give predictions and recommendations. Examples of such applications include risk analysis, fraud detection, and prevention, personalized health services, etc.

This course focuses on covering the fundamentals like Statistics, probability, and a variety of machine learning algorithms that form the building blocks for the development of intelligent applications.



Is This The Right Course?

  • Basic familiarity with Python programming.
  • Basic understanding of Data Terminologies.
  • Familiarity with enterprise IT.
  • Foundational knowledge in mathematical concepts like linear algebra and probability.
  • Basic linux skills.
  • Basic SQL skills.

Who Should Attend?

This is an introduction-level hands-on course suitable for everyone who wants to explore the field of Artificial Intelligence (AI) and Machine Learning (ML). This course covers the foundations of AI, ML, programming, search, and logic along with their applications to computational problems. This is a level 1 course in building your skills for developing Intelligent applications using Machine Learning and Artificial Intelligence. Anyone who wants to shift their career to AI and ML can attend this course such as

  • Business Analysts
  • Data Analysts
  • Developers
  • Administrators
  • Architects
  • Managers
  • Anyone new to AI and ML who wants to understand the foundations of ML and AI for developing ML applications.

  • Basic familiarity with Python programming.
  • Basic understanding of Data Terminologies.
  • Familiarity with enterprise IT.
  • Foundational knowledge in mathematical concepts like linear algebra and probability.
  • Basic linux skills.
  • Basic SQL skills.

  • Explore Jupiter notebooks and Python
  • Recall and Remember Statistics and Probability concepts and explore coding in Python for data analysis
  • Understand advanced probability concepts and data visualizations using libraries such as Matplotlib, seaborn
  • Understand various Machine Learning Algorithms and their applications
  • Understand various Predictive Models
  • Apply Machine Learning algorithms to build predictive models
  • Understand Recommendation Systems
  • Understand how to deal with data in the real world
  • Apply Machine Learning on Big Data using Apache Spark
  • build UI and REST APIs for ML models

  1. Getting Started with Jupitor Notebooks and Python
    • Installing Python 3.x and Data Science environment
    • Importing required modules
    • Writing and executing Python code in Jupiter notebook
    • Understanding Data Visualizations using Matplotlib and seaborn.
    • Running Python scripts
    • Statistics and Probability Essentials
      • Understanding Descriptive and Inferential Statistics and the difference between them
      • Understanding Data types - quantitative and qualitative
      • How to understand the spread of data with measures of Central Tendency and dispersion
      • Understanding dirty data - missing values and outliers
      • Understanding probability distributions, Probability density function and probability mass function
      • Understanding the spread and distribution of data using Python.
      • Introduction to Percentiles and Moments and why they are important?
      • Understanding Hypothesis Testing and various types of Hypothesis tests with their applications.
    • Advanced Probability Concepts
      • Covariance and Correlation and their role in understanding Data
      • How Conditional Probability helps in predictive analytics?
      • Baye's Theorem and it's applications
  2. Machine Learning Algorithms
    • Understanding Different Types of Machine Learning Algorithms - Supervised, Semi-Supervised, UnSupervised, Reinforcement Learning
    • Distinguish between Linear and Non-Linear, Distance-based, Parametric and Non-Parametric machine learning models
    • Understanding different phases of building Machine Learning Models
    • Differentiate between Classification and Regression
    • An overview of linear and logistic regressions
    • An overview of decision trees and random forests
    • An overview of KNN and SVM
    • How to Build Predictive Models with available data?
      • Understanding your data - Data loading and descriptive analysis
      • Dealing with unclean data - Data Cleaning and Pre-processing
      • Building machine learning models with Linear Regression and Logistic Regression
      • Understand when to apply Polynomial Regression and build a model
      • Building a predictive model using multivariate regression
      • Multi-level models
  3. Evaluating and Tuning your models with advanced Machine Learning with Python
    • Understanding model fit - overfitting and underfitting, Bias-Variance Trade-Off
    • Understanding model evaluation metrics for regression and classification
    • Understanding K-fold cross-validation to avoid overfitting
    • Bayesian models
    • Implementing Email spam classifier with Naïve Bayes Classifier
    • Understand K-Nearest Neighbors Algorithm and using KNN for predictive analytics
    • Understand Gradient Descent, Stochastic Gradient Descent, and tune your model
    • Understand ensemble methods, bagging and boosting
    • Understand various boosting algorithms
    • Unsupervised Machine Learning
      • Understanding Clustering
      • Understand K-Means Clustering with a case study
      • Understanding Dimensionality Reduction and Principal Component Analysis
      • Applying PCA on a real world dataset
    • Understanding and Building Recommendation Systems
      • What are recommendation Systems
      • Understanding User-based and Item-based Collaborative Filtering
      • Finding similar movies
      • Improving the results of movie similarities
      • Making movie recommendations to people
      • Improving our recommendation results
Contact us for more detail about our trainings and for all other enquiries!

Upcoming Trainings

Join our public courses in our Finland facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

24 tammikuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
16 helmikuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
24 tammikuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
16 helmikuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
12 maaliskuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
13 maaliskuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
12 maaliskuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
13 maaliskuuta 2025 (3 Days)
Helsinki, Espoo
Classroom / Virtual Classroom
Building Intelligent Applications with AI and ML - Level 1 Training Course in Finland

Finland is a country located in northern Europe. Helsinki is the capital and largest city of the country. The majority of the people are Finns but there is also a small Lapp population in Lapland, where the country is famous for the Northern Lights. Finland's national languages are Finnish and Swedish.

Known for its vast forests, lakes, and natural beauty, Finland is one of the world's largest producers of forest products, such as paper, pulp, and lumber. One of the world's largest sea fortresses Suomenlinna, Rovaniemi with the "White Nights", dogsled safaris and of course the Northern Lights are what makes Finland so popular for tourists. Finland is one of the best places in the world to see the Northern Lights and attracts millions of tourists during its seasons.

Finland is home to a thriving technology industry and is widely recognized as one of the world's leading technology hubs. Companies such as Nokia and Rovio (creator of the popular game Angry Birds) are based in Finland. Some of the key factors that have contributed to Finland's success in technology include; strong investment in research and development, a highly educated workforce and fundings.

Finland has a strong educational system, and is widely regarded as one of the world's most literate countries. In fact, Finland's literacy rate is one of the highest in the world, and its students consistently perform well in international tests of math and reading ability.

Also, as a pioneer in environmental sustainability, Finland is known for its efforts to reduce its carbon footprint and promote clean energy. This Nordic country is also famous for its unique and distinctive cultural heritage, including its traditional folk music and its elaborate traditional costumes.

Helsinki, Finland's capital city, is the country's business center. Helsinki is Finland's largest city, and it is home to many of the country's major corporations and organizations, including many of the country's leading technology firms. The city is also a commercial, trade, and financial center, as well as one of the busiest ports in the Nordic region.

Take advantage of our diverse IT course offerings, spanning programming, software development, business skills, data science, cybersecurity, cloud computing and virtualization. Our knowledgeable instructors will provide you with practical training and industry insights, delivered directly to your chosen venue in Finland.

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