Introduction
The world of artificial intelligence (AI) is changing rapidly, and its two key branches—Machine Learning (ML) and Deep Learning (DL)—have become the foundation for modern technologies: from recommendation systems to autonomous vehicles. If you want to master these areas but don't know where to start, the course "Machine Learning and Deep Learning" on the ASI Biont platform is your ideal starting point. We'll break down how AI helps in learning and why this course is a unique opportunity to dive into the topic without financial investment.
What are Machine Learning and Deep Learning?
Machine Learning is a subfield of AI where algorithms learn from data, identifying patterns without explicit programming. Deep Learning is a deeper branch that uses neural networks with many layers (hence the name "deep learning"). These technologies allow solving complex tasks: image recognition, natural language processing (NLP), and time series forecasting.
Key Differences
| Parameter | Machine Learning | Deep Learning |
|---|---|---|
| Data volume | Thousands of examples sufficient | Millions of examples required |
| Computational resources | CPU-sufficient | GPU/TPU required |
| Typical tasks | Classification, regression, clustering | Text generation, speech recognition, computer vision |
| Example tools | Scikit-learn, XGBoost | PyTorch, TensorFlow |
How AI Helps in Learning on the ASI Biont Course?
The ASI Biont platform uses generative AI to create personalized lessons. These are not just static materials—AI adapts content to your knowledge level. For example, if you are a beginner, the system starts with the basics of linear algebra, and if you are already familiar with Python, it immediately moves on to building models. This approach accelerates material mastery and makes learning with AI as effective as possible.
Practical Tools in the Course
The course covers the main libraries and platforms used in the industry:
- Scikit-learn — for classic ML algorithms: linear regression, decision trees, SVM.
- PyTorch — for flexible neural network construction and research.
- TensorFlow — for production solutions and model deployment.
- Kaggle — for working with real datasets and competitions.
For example, you will learn to implement a model for predicting housing prices using Scikit-learn, and then delve into transformers (BERT, GPT) on PyTorch for text sentiment analysis.
From Linear Regression to Transformers
The course is built on the principle "from simple to complex." Starting with linear regression, you will gradually move on to neural networks and modern architectures such as transformers. This is important because transformers are the basis of ChatGPT, DALL-E, and other generative models.
Learning Stages
- ML Basics: mathematical statistics, quality metrics (MSE, accuracy), overfitting and regularization.
- Classic Algorithms: logistic regression, random forest, gradient boosting.
- Neural Networks: multilayer perceptron, backpropagation, activation functions.
- Convolutional Networks (CNN): for working with images and video.
- Recurrent Networks (RNN) and LSTM: for sequential data.
- Transformers: attention mechanism, BERT, GPT—the final stage.
Deploying Models to Production
One of the key topics of the course is how to turn a model from an experimental prototype into a working service. You will learn:
- How to save and load trained models (pickle, ONNX).
- How to use Docker and FastAPI to create an API.
- How to monitor model performance after deployment.
This stage is critical for your career: employers value specialists who not only build models but can also implement them into real business processes.
Why Choose ASI Biont?
- 100% free: all courses on the platform are completely free with no hidden fees or freemium model.
- AI-generated lessons: content is created for your
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