Online course
"Intro to Machine Learning"
"Intro to Machine Learning"
Learn the most popular ML algorithms in 10 lessons.
We start on 1 Mar.
Price: $115
We start on 1 Mar.
Price: $115
We developed this course in order to help you dive into the Machine Learning world
Start developing your own ML models just now.
Regression
You'll understand the most popular ML algorithms: linear and logistic regression.
Neural Networks
You'll develop and train your first neural network from scratch.
Python Libraries
You'll learn to use the Python libraries for the ML tasks.
Who will benefit from this course?
Those of you who work in IT and want to expand horizons by learning the ML technologies.
Those of you who have base knowledge in ML and want to systematize them.
Those of you who want to understand how the ML technologies can help work with your data (e.g., product managers, business analysts).
Program
1
Introduction.
The ML definitions. Principal differences between the ML algorithms and any other ones. Classification of the ML algorithms and scopes of their practical application. Fundamental concepts of the ML. Introduction into Python. The sklearn library.
2
Mathematical background of the Machine Learning.
Introduction into the matrices and matrix calculations. Acceleration of operations with data by use of vectorization. Functions minimization as a main ML operation. The gradient descent method. Preparation of the inputs for the ML algorithms.
3
Linear regression.
Linear regression as a predicting algorithm. Cost function of the linear regression. Evaluation of the predictions accuracy. Working with multidimensional data.
4
Logistic regression.
Logistic regression as a classification algorithm. Cost function of the logistic regression. Evaluation of the classification accuracy. Working with multidimensional data. Multiclass logistic regression.
5
Clusterization.
General approaches to the data clusterization. K-means and C-means algorithms. Evaluation of the clusterization accuracy. Working with multidimensional data.
6
Introduction to neural networks.
The neural networks fundamentals. Base terminology: neurons, layers, activation, forward and backward propagation. Logistic regression as a specific case of a neural network.
7
Objects classification with the neural networks.
Cost function for a neural network. Different activation functions (sigmoid, tanh, ReLU), scopes of their application. Implementation of the forward & backward propagation algorithms. Evaluation of the classification accuracy. Working with multidimensional data.
8
Tuning and accuracy control of the ML algorithms.
Typical problems of the ML algorithms: high bias (underfitting) and high variance (overfitting). Training and test datasets. Learning curves. Regularization of the ML algorithms. Metrics for evaluation of the ML algorithms accuracy.
The best terms for your studying
Quality assurance
You will be taught by a certified professional with many years of teaching experience.
Free consultation
Get one personal 30-minute consultation
with a teacher for free.
with a teacher for free.
Certificate
You will receive a certificate of completion of the Machine Learning courses.
10
lessons
5+
the most popular ML algorithms
$115
total price of the course
Our teacher
Our teacher is Alexander Pevzner, theoretical physicist, team lead in OWOX, certified professional in Machine Learning. During last two years, Alexander has been organizing various workshops, trainings, and courses concerning different aspects of the ML applications.
Testimonials
Learn Machine Learning with us
Price: $115
Registration
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