Machine Learning and Deep Learning - course 68,040 rub. from SkillFactory, training 20 weeks, Date: August 13, 2023.

Brief program of the course “Machine Learning PRO”

Module 1
Introduction to Machine Learning
We get acquainted with the main tasks and methods of machine learning, study practical cases and apply the basic algorithm for working on an ML project
We solve 50+ problems to reinforce the topic

Module 2
Data preprocessing methods
We study data types, learn to clean and enrich data, use visualization for preprocessing and master feature engineering
We solve 60+ problems to reinforce the topic

Module 3
Regression
We master linear and logistic regression, study the limits of applicability, analytical inference and regularization. Training regression models
We solve 40+ problems to reinforce the topic

Module 4
Clustering
We master learning without a teacher, practice its various methods, work with texts using ML
We solve 50+ problems to reinforce the topic

Module 5
Tree-based algorithms: an introduction to trees
Let's get acquainted with decision trees and their properties, master trees from the sklearn library and use trees to solve a regression problem
We solve 40+ problems to reinforce the topic

Module 6
Tree-based algorithms: ensembles
We study the features of tree ensembles, practice boosting, use the ensemble to build logistic regression
We solve 40+ problems to reinforce the topic
We are participating in a competition on kaggle for training a tree-based model

Module 7
Assessing the quality of algorithms
We study the principles of sample splitting, under- and overfitting, evaluate models using various quality metrics, learn to visualize the learning process
Evaluating the quality of several ML models
We solve 40+ problems to reinforce the topic

Module 8
Time series in machine learning
Let's get acquainted with time series analysis in ML, master linear models and XGBoost, study the principles of cross-validation and parameter selection
We solve 50+ problems to reinforce the topic

Module 9
Recommender systems
We study methods for constructing recommender systems, master the SVD algorithm, evaluate the quality of recommendations of the trained model
We solve 50+ problems to reinforce the topic

Module 10
Final hackathon
We apply all the studied methods to obtain maximum accuracy of model predictions on kaggle

Course program "Deep Learning"

Module 1
Introduction to Artificial Neural Networks
We create a neural network for recognizing handwritten numbers in Python

Module 2
Frameworks for deep learning (TensorFlow, Keras)
We create an image recognition model based on the FashionMNIST dataset and the Keras framework

Module 3
Convolutional Neural Networks
We recognize images in the CIFAR-10 dataset using a convolutional neural network

Module 4
Neural network optimization
Improving the speed and performance of networks for the case of the previous module

Module 5
Transfer learning & Fine-tuning
Additional training of the ImageNET neural network to solve the problem of image classification

Module 6
Image segmentation
Designing a neural network for segmenting people in the COCO dataset

Module 7
Object detection
We train a neural network to solve a detection problem using the example of a dataset with brand logos

Module 8
Introduction to NLP and Word Embeddings
Creating a neural network for working with natural language

Module 9
Recurrent neural networks
Creating a chatbot based on a recurrent neural network

Module 10
Reinforcement Learning
Creating an agent for playing Pong based on the DQN algorithm

Module 11
What's next?
Let's get acquainted with other areas of application of neural networks. Creating a GAN neural network for image generation