Machine Learning with Python is the development and implementation of machine learning algorithms using the Python programming language.<\/p>\n
Python is a popular language choice for machine learning because it is easy to learn, readable and has many libraries that are essential for machine learning.<\/p>\n
Python has also become the dominant language in machine learning and data science. Python is widely used to fit complex models to scattered datasets.<\/p>\n
Machine learning is a scientific field of study in which various techniques are developed to try to make computers learn in a similar way to humans.<\/p>\n
In programming terms, machine learning is just a few lines of code. With this course you will be able to understand the theory, logic, model setup and optimization. The training covers regression and classification models, including tree-based methods, clustering and sparse regression models.<\/p>\n
In addition, modern data science and machine learning concepts and models are discussed with the basics of programming and statistics; it is aimed to gain practicality with applications and examples.<\/p>\n
The advantages of Machine Learning with Python include increased productivity, faster development, and better results.<\/p>\n
Machine Learning with Python can process large data sets and generate accurate predictions by recognizing complex patterns.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”3%” overlay_strength=”0.3″ shape_divider_position=”bottom” bg_image_animation=”none” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” overlay_strength=”0.3″ column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][toggles style=”minimal”][toggle color=”Extra-Color-2″ title=”About Training”][vc_column_text]Training Objectives:<\/p>\n
<\/p>\n
Training Content:<\/p>\n
Classification with KNN<\/p>\n
Regression with KNN<\/p>\n
Decision Trees<\/p>\n
Random Forests<\/p>\n
Classification with Random Forests<\/p>\n
Regression with Random Forests<\/p>\n
Decision Support Machines<\/p>\n
Classification with Decision Support Machines<\/p>\n
Regression with Decision Support Machines<\/p>\n
Gradient Boosting<\/p>\n
Classification with Gradient Boosting<\/p>\n
Regression with Gradient Boosting<\/p>\n
Introduction to machine learning<\/p>\n
Introduction to related packages in Python (Numpy, Scipy and SciKit-Learn)<\/p>\n
Learn the why and how of preprocessing your data with scaling transformations and coding<\/p>\n
Typical standardization and normalization procedures<\/p>\n
How to move from a linear regression and statistical model to a machine learning model?<\/p>\n
Introduction to modeling techniques<\/p>\n
Measuring Modeling Effectiveness (Verification and testing; techniques such as cross validation)<\/p>\n
Criteria that can be used to judge the model and the different criteria that are appropriate for evaluation<\/p>\n
Techniques for performing feature selection<\/p>\n
Unsupervised learning technique to uncover patterns and structure in data<\/p>\n
Some more advanced model fitting using algorithms such as gradient boosted trees and support vector machines<\/p>\n
What is Unsupervised Learning?<\/p>\n
Kmeans<\/p>\n
Spectral Clustering<\/p>\n
Mean-shift<\/p>\n
Affinity Propagation<\/p>\n
How to Measure the Performance of Clustering Algorithms?[\/vc_column_text][\/toggle][toggle color=”Extra-Color-2″ title=”Who Should Receive the Training?”][vc_column_text]\n
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