Who this course is for
For those who want to master a new profession
This course is perfect for those who want to master a new profession in the world of artificial intelligence (AI) and earn from $2000 per month. The course provides extensive knowledge and skills that will allow you to confidently start working with AI and understand key machine learning algorithms. You will learn how to apply AI in practice and solve real business problems. You will learn from scratch how to develop complex systems, algorithms, automation of business processes for any industry, which will allow you to work anywhere in the world with any business, with any client you want. With this profession, you won't be chosen, but you will choose who to work with and how much to earn.
Who wants to optimize routine tasks
The course is useful for specialists who seek to improve their work processes and reduce routine with the help of AI. In the course, you will learn to use AI tools to automate tasks such as data analysis, forecasting, and information processing. This will allow you to focus more on strategic tasks, instead of spending time on routine. You will be able to automate and delegate up to 90% of routine tasks to your own AI assistants, which will allow you to save up to 10 working hours per week, increase your productivity and multiply your income.
Business owners who want to automate business processes
For business owners, the course will be an excellent opportunity to understand how to use AI to optimize and automate various business processes. You will learn to apply AI to improve customer service, marketing, and data management, which will help increase business efficiency, reduce costs, and increase profits. The course is focused on the practical use of AI to solve real problems faced by entrepreneurs.
You will also learn and understand what kind of specialist to hire, how to check them and understand if they are not "pulling the wool over your eyes" for the development of such systems.
What you will learn and what results you will achieve in the course:
This course will open a world of opportunities for you and help you feel confident in a world where AI plays an increasingly important role. You won't just learn something new — you'll get real tools that you can use right away!
To pricing- Master the basics of Python programming — a simple and popular language that will be your first step into the world of artificial intelligence.
- Understand how AI algorithms work — learn how to apply them to real tasks and process automation. This will help you simplify work tasks and do them faster.
- Create your first AI models — you will learn to analyze data, predict results, and test your models. You will see how your ideas turn into working tools.
- Learn to solve business problems with AI — you will be able to apply AI in your professional field, whether it's finance, marketing, sales, or any other area.
- Overcome the fear of AI — gain confidence in your skills, learn to apply AI in everyday work and personal projects.
- Be ready for new challenges in the labor market — learn how AI can make you a sought-after specialist and help you develop in the digital economy.
- Learn to find clients, negotiate with them, and sell your knowledge and AI development services.
Course outcome:
Practical consolidation of knowledge and ability to implement a project, as well as a project portfolio that can be demonstrated to an employer. Knowledge of how to find clients and how to earn from $2000.
Course Program
Introduction to artificial intelligence and machine learning
Introduction to artificial intelligence and machine learning
Lesson 1: What is artificial intelligence and its main types
What you will learn:
- We'll explore what AI is and what its main types are: narrow, general, superintelligence.
Additional materials:
- Kaggle-project: Titanic: Machine Learning from Disaster (perfect start for understanding machine learning).
- Mind map: "Main types of AI and their examples".
- PDF-guide: "10 examples of AI in everyday life".
Lesson 2: History and development of AI
What you will learn:
- How AI has evolved from the ideas of ancient philosophers to modern technologies.
Additional materials:
- Mind map: "Stages of AI development".
- Checklist: "5 key events in AI history".
- Kaggle: Explore historical data (Historical Data Visualization).
Lesson 3: Examples of AI application in different industries
What you will learn:
- How AI helps in medicine, transportation, business, and other fields.
Additional materials:
- Kaggle-project: House Prices (practical use of data analysis).
- Mind map: "Fields of AI application".
- Infographic: "Where AI is used today".
Working with data
Working with data
Lesson 4: Collection, cleaning and preparation of data
What you will learn:
- Why data is the foundation of AI. Learn to clean and prepare it.
Additional materials:
- Kaggle-project: Clean Dataset Challenge (data cleaning and analysis).
- Mind map: "Stages of working with data".
- Guide: "How to clean data step by step".
Lesson 5: Types of data
What you will learn:
- We'll examine numerical, textual, and categorical data.
Additional materials:
- Infographic: "Types of data and their examples".
- PDF-guide: "Secrets of working with text and numerical data".
- Kaggle-project: Text Classification Basics.
Lesson 6: Basics of data visualization
What you will learn:
- How to create charts and visualizations to better understand data.
Additional materials:
- Mind map: "Best visualization methods".
- Checklist: "Creating clear charts".
- Kaggle-project: Visualization Challenge.
Basic machine learning algorithms
Basic machine learning algorithms
Lesson 7: Linear regression
What you will learn:
- How to predict results, for example, the cost of an apartment.
Additional materials:
- Infographic: "How linear regression works".
- Kaggle-project: Boston Housing Price Prediction.
- Mind map: "Steps of building linear regression".
Lesson 8: Logistic regression
What you will learn:
- How to classify data, for example, spam and not spam.
Additional materials:
- Video: "How logistic regression works".
- Kaggle-project: Spam Detection.
- Guide: "Classification in practice".
Lesson 9: K-nearest neighbors
What you will learn:
- An algorithm that makes predictions based on similar data.
Additional materials:
- Infographic: "Example of KNN work".
- Kaggle-project: Fruit Classification Dataset.
- Mind map: "How KNN works".
Decision trees and random forests
Decision trees and random forests
Lesson 10: Understanding decision trees
What you will learn:
- How algorithms make decisions through sequential questions.
Additional materials:
- Mind map: "How a decision tree is built".
- Kaggle-project: Heart Disease Prediction.
- Guide: "Step-by-step work with decision trees".
Lesson 11: Random forest
What you will learn:
- How trees are combined to improve accuracy.
Additional materials:
- Infographic: "What is a random forest".
- Kaggle-project: Random Forest Classifier Challenge.
Reinforcement learning
Reinforcement learning
Lesson 12: Basics of reinforcement learning
What you will learn:
- How AI learns through mistakes, for example, teaching a robot to walk.
Additional materials:
- Kaggle-project: Game AI Basics.
- Mind map: "How reinforcement learning works".
- Video: "AI and games: reinforcement learning".
Introduction to neural networks
Introduction to neural networks
Lesson 13: What are neural networks and how they work
What you will learn:
- A simple explanation of how neural networks are inspired by the work of the human brain. You'll understand how they find patterns in data.
Additional materials:
- Mind map: "How neural networks are structured".
- Kaggle-project: Digit Recognizer (handwritten digit recognition).
- Video: "Neural networks in 5 minutes".
Lesson 14: Principles of deep learning
What you will learn:
- What distinguishes deep neural networks from regular ones. You'll learn how they work with images and text.
Additional materials:
- Infographic: "How deep neural networks work".
- Kaggle-project: Cats vs Dogs (image recognition).
- Video: "Examples of deep learning in practice".
Lesson 15: Introduction to libraries for working with neural networks
What you will learn:
- Basics of using TensorFlow and PyTorch. You'll understand how to run ready-made projects.
Additional materials:
- Guide: "How to install and get started with TensorFlow".
- Kaggle-project: Simple Neural Networks for Beginners.
- Video: "Quick start with TensorFlow".
Model evaluation and improvement
Model evaluation and improvement
Lesson 16: Model evaluation metrics
What you will learn:
- Learn to evaluate model quality using accuracy, completeness, and F1-score.
Additional materials:
- Mind map: "How evaluation metrics work".
- Kaggle-project: Model Evaluation Basics.
- Guide: "Step-by-step instruction for calculating metrics".
Lesson 17: Hyperparameter tuning
What you will learn:
- How to improve a model by changing its settings (e.g., learning rate).
Additional materials:
- Kaggle-project: Hyperparameter Tuning in Practice.
- Infographic: "Which hyperparameters are important?".
- Video: "Hyperparameter tuning for beginners".
Lesson 18: Cross-validation
What you will learn:
- How to test models on different datasets to avoid errors.
Additional materials:
- Guide: "What is cross-validation and why it's needed".
- Kaggle-project: Cross-validation in ML.
- Mind map: "Cross-validation steps".
Working with big data
Working with big data
Lesson 19: Basics of working with big data
What you will learn:
- Learn what big data is, where it comes from and how it's used.
Additional materials:
- Mind map: "Main sources of big data".
- Kaggle-project: Big Data Analytics.
- Guide: "What you need to know about big data".
Lesson 20: Tools for working with big data
What you will learn:
- Get acquainted with popular tools: Hadoop and Spark.
Additional materials:
- Infographic: "Comparison of Hadoop and Spark".
- Kaggle-project: Introduction to Big Data Tools.
- Video: "How Hadoop and Spark work".
Application of AI in various industries
Application of AI in various industries
Lesson 21: AI in finance
What you will learn:
- How AI helps detect fraud, predict risks, and analyze markets.
Additional materials:
- Mind map: "Application of AI in finance".
- Kaggle-project: Credit Card Fraud Detection.
- Video: "AI in finance: examples and cases".
Lesson 22: AI in healthcare
What you will learn:
- How AI diagnoses diseases and helps develop medicines.
Additional materials:
- Case study: "Example of using AI in diagnostics".
- Kaggle-project: Medical Data Analysis.
- Infographic: "AI in medicine".
Lesson 23: AI in marketing and sales
What you will learn:
- How AI predicts sales and helps personalize advertising.
Additional materials:
- Checklist: "AI tools for marketing".
- Kaggle-project: Customer Segmentation Analysis.
- Video: "AI in marketing: real examples".
Ethics and responsibility in AI
Ethics and responsibility in AI
Lesson 24: Basic principles of AI ethics
What you will learn:
- Why it's important to consider ethics when developing AI.
Additional materials:
- Mind map: "Basics of ethics in AI".
- Checklist: "How to avoid mistakes in AI ethics".
- Video: "Ethical issues in AI".
Lesson 25: Responsible use of AI
What you will learn:
- How to minimize risks and protect users.
Additional materials:
- Guide: "How to safely use AI".
- Video: "Developers' responsibility".
- Case study: "Real examples of errors in AI".
Lesson 26: Rights and obligations of developers
What you will learn:
- What responsibility AI creators bear.
Additional materials:
- Infographic: "Rights and obligations in AI development".
- Video: "How developers can minimize risks".
Basics of AI for financial analysis
Basics of AI for financial analysis
Lesson 27: Predicting financial risks
What you will learn:
- How AI helps assess credit and investment risks.
Additional materials:
- Kaggle-project: Credit Risk Analysis.
- Infographic: "Risk analysis steps".
- Video: "AI in financial risk management".
Lesson 28: Fraud analysis methods
What you will learn:
- How AI detects fraudsters in financial systems.
Additional materials:
- Kaggle-project: Fraud Detection Basics.
- Checklist: "Signs of fraud that AI considers".
- Video: "Examples of fraud analysis with AI".
Practical cases and final project
Practical cases and final project
Lesson 29: Final project
What you will learn:
- How to combine knowledge from all modules and create your project.
Additional materials:
- Kaggle-project: Full cycle of ML project development.
- Checklist: "Stages of creating an AI project".
- Mind map: "How to build a final project".
Reviews
Pricing
Starter
All lessons described in the program with additional materials for self-study, without feedback, with access for 6 months
200$ 350$
*with the possibility of installment paymentAdvanced
All lessons described in the program with additional materials.
-
Personal AI assistant that will help in training, answer questions, check homework, give recommendations on studying, on career building.
-
Access to the course for 1 year.
300$ 450$
*with the possibility of installment paymentMaximum
All lessons described in the program with additional materials.
-
Personal AI assistant that will help in training, answer questions, check homework, give recommendations on studying, on career building.
-
Curator for 3 months
-
Sessions personally with Valentin with answers to your questions, help in training, recommendations for career building, further training and development after the course
-
Closed community of graduates in Telegram with exclusive information, reviews of new neural networks and lots of additional closed information
-
Once a month, analysis of Kaggle tasks in the format of recorded video with additional materials
-
Access to the course forever
350$ 600$
*with the possibility of installment paymentFAQ
How can I pay?
Payment is available by any convenient method:
- Full payment at once.
- Payment in parts from the bank
- Payment in parts personally from the course founder
- Payment with cryptocurrency
Leave a request and our manager will answer and advise on all payment options
How do lessons work?
All lessons are recorded and prepared in advance and you will be able to study when it's convenient for you. Depending on the tariff, there will also be live broadcasts personally with the founder of the course.
Will there be homework checking?
Depending on the tariff. On the "Starter" tariff you will not have checking, on all others you will have a personal AI assistant who will help you, check homework, give feedback, answer questions. There will also be feedback from the curator.
How long does the course last?
The full basic course is designed for 3 months, but you can take it at such a pace and with such speed as is convenient for you
Additional lessons from tariffs for specialists and for entrepreneurs are designed for an additional one month each.
How long will I have access?
The period of access to the course depends on the chosen tariff. From 6 to 24 months.
The first ever course, created entirely by artificial intelligence under human guidance - Valentin
If you have additional questions, or if you want to reserve the current cost, leave a request and our manager will answer all your questions, help you choose a tariff and reserve the cost for you