The course will take you to an in-depth knowledge of the various concepts including:
Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
And many other useful concepts.
Also, the course will take you to numerous case studies and applications. At the end of the course, you will able to learn how to apply Machine learning algorithms to build smart robots, text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other many other useful areas in Machine Learning. The course is very informative and very popular among machine learning enthusiasts.
The course will take you to an advanced understanding of the various concepts in deep learning, reinforcement learning, natural language understanding, computer vision, and Bayesian methods. The course has its relevance and importance in the way that the top Kaggle machine learning practitioners and CERN scientists will share their experience in the course of solving real-world problems which will help you to fill the gaps between theory and practice and will make you learn from the industry experts.
There are 7 courses in this specialization which includes:
Introduction to Deep Learning
How to win a Data Science competition
Bayesian methods of machine learning
Practical Reinforcement learning
Deep Learning in computer vision
Natural Language Processing
Addressing Large hadron collider challenges through machine learning
In this course, you will learn various Machine Learning models and methods to apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams and plotting the path of movie zombies.
Highlights of the course:
Probabilistic versus non-probabilistic modeling
Supervised versus unsupervised learning
Various topics including classification and regression, clustering methods, sequential models, matrix factorization, topic modeling, and model selection are discussed in the course which will take you to an in-depth understanding of the various essentials of Machine Learning. Also the various machine learning methods like linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models are discussed in the course with in-
depth analysis which will prove the course very useful to every beginner and advanced.
The course will make you learn how to perform Machine Learning with Python. You will learn how to create models using various machine learning algorithms in the course. At the end of this course, you will be able to use Python and the sci-kit-learn library to create Machine Learning solutions. Also, you will understand how to evaluate and improve the performance of the solutions you create. The course involves some understanding of the basic statistics and some background in software development. The learning offered by the course is simple that every beginner and advanced with some background in statistics will understand the course easily.
Apply Convolutional Neural Networks and Recurrent Neural Networks in practice
the intuition behind Auto-Encoders
the intuition behind Self-Organizing Maps
to Apply Auto-Encoders in practice
to apply Boltzmann Machines in practice
Basic Python programming knowledge with some basic background in Mathematics is needed for the course to undertake by any beginner. The course contains the most useful concepts explained in the most lucid way.