Background Zero to Hero Data Science You will learn to get valuable information from unprocessed data to make strategic decisions.

Build your future career on data analysis, machine learning and neural networks.

The opportunity to work with the support of partner companies after completing course
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You will become a desirable specialist in the field of Data Science learning courses with new knowledge

Data Science courses in Baku help to discover the criteria that remain hidden in the field of business by creating and developing predictive models through data science machine learning algorithmsin Azerbaijan and neural networks as a complex programming course and to improve event management and business processes. You will not only take big analytics courses, help companies solve their daily problems through neural networks and Big Data analysis technologies, at the same time you will learn to understand the basic principles of teamwork, purposefulness, and emotional intelligence skills. This course gives you all the necessary opportunities to move from programmer to Data Science and the big Analytics field. A large number of practical jobs, different business cases, and new acquaintances await you at Data Science courses.

You will not only gain new knowledge, at the same time you will get a chance to win valuable prizes by participating in competitions on different topics held every month during programming courses under the supervision of experienced teachers. The main thing is that the experience you gained during the lesson will give you an invaluable success in your future career.

This data science machine learning course will teach you

data science and machine learning azerbaijan and baku

Teaching prosess

You will not be faced with innovations during data science machine learning: individual consultations by the mentor during the study period, professional support in all developed projects, working experience with the team leader during teamwork.

It will be easy to understand what algorithmic thinking is by optimizing the algorithms that you have built to solve insignificant problems.

You will discover new knowledge through various experiments iin data science as well as machine learning such as simulators, homework, interactive webinars, individual and team projects.

Machine Learning Course Programme

  • Basics of data
  • Functions and classes
  • Advanced data types: arrays, multiples, dictionaries
  • Libraries for data analysis: NumPy, Pandas, Matplotlib
  • Visualization via Python
  • Basics of Statistics
  • Random events, Random variables
  • Logistic regression and discriminatory analysis
  • Correlation and correlation analysis
  • Confidence intervals. Statistical hypothesis tests
  • Linear algebra. Vectors
  • Linear algebra. Matricians
  • Linear algebra and other topics
  • Mathematical analysis, törəmə 
  • Bir neçə arqumentdən ibarət funksiyanın törəməsi
  • Theory of optimization
  • Probability theory. Separate and continuous random variables
  • Central limit Theorem and multiple laws
  • Architecture and structure of the database
  • Simple survey. Aggregates
  • Main commands and related analytical functions in SQL
  • İmport and export of data through SQL and ETL applications
  • Principles of working with databases
  • Basic libraries required to connect to databases via Python
  • Functions of SQL and its analogues in pandas
  • Konsol (dating, major operators, psqlutility)
  • Architecture and design
  • Normalization
  • Dependencies
  • Regression analysis. Linear, polynomial and logarithmic regression
  • Klasifikasiya: məntiqi reqresiya və SVM
  • Zərər funksiyaları və optimallaşdırma
  • Evaluation, retraining and adjustment of model accuracy
  • Data quality problems
  • Working with gaps and variables
  • Access to Neural Network and Tensor Flow library
  • Neural Network and Tensor Flow library, more detailed
  • Introduction to neural network
  • Introduction to recurrent network
  • Auto encoders
  • Introduction to Generative adversarial network
  • Regression and Perceptron
  • Multilayer neural network: regulation, gradient descent, acceleration of learning
  • Convolutional networks: convolutional architectures, multidimensional convolutions, segments
  • Recurrent networks: RNN, GRU и LSTM, Encoder-Decoder architecture
  • Attention: Dense Attention and Beam search
  • Computer vision: SSD, Region Based CNN, Faster R-CNN, Masked R-CNN, UNet, stil transfer and FCN
  • Text processing: language models, Embeddings, Word2Wec, FastText, NER, Transformer, BERT and Elmo
  • GANs: discriminator, generator, more advanced architectures
Data Analysis

First step to become a Data Scientist