Students who have at least high school knowledge in maths and who want to start learning Machine Learning.Īny intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.Īny people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.Īny students in college who want to start a career in Data Science.Īny data analysts who want to level up in Machine Learning.Īny people who are not satisfied with their job and who want to become a Data Scientist.Īny people who want to create added value to their business by using powerful Machine Learning tools. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Moreover, the course is packed with practical exercises that are based on real-life examples.
Natural Language Processing: Bag-of-words model and algorithms for NLPĪrtificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16, Transfer learning, Web Based Flask Application.
Regression: Simple Linear Regression,, SVR, Decision Tree, Random Forest,Ĭlustering: K-Means, Hierarchical Clustering AlgorithmsĬlassification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification It is structured the following way:ĭata Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc This course is fun and exciting, but at the same time, we dive deep into Machine Learning.
#ACTUAL MAP OF THE WORLD SERIES#
There are 4 different sections in this course for complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, Time Series Analysis. So, In this course also you will able learn Basics of Python to Advance State of the Art Techniques of Deep Learning Models. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms. Machine learning can help with the diagnosis of diseases.
#ACTUAL MAP OF THE WORLD HOW TO#
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.Ī Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.īelow are few Applications of Machine Learning in Practical Real World Interested in the field of Machine Learning? Then this course is for you!
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