Machine Learning Algorithms - PDF eBook Now just $5

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As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.


Machine Learning Algorithms - Giuseppe Bonaccorso

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My latest machine learning book has been published and will be available during the last week of July. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems.


Top 20 Data Science MOOCs

@machinelearnbot

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).


Top 20 Data Science MOOCs

@machinelearnbot

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).


What is Machine Learning? Machine Learning Basics Machine Learning Tutorial CloudxLab

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Real-life Projects so that you can apply the skills learnt during the course - - - - - - - - - - - - - - Who should go for this course? This course is for anyone who wants to become expert in Machine Learning, Deep Learning, Data Science and progress in the career. Ideally, this course will help professionals in the following groups 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or Artificial Intelligence 4. Recent graduates passionate about building a successful career in Data Science and Machine Learning - - - - - - - - - - - - - - Why Learn Machine Learning and Deep Learning? In the recent times, it has been proven that Machine Learning and Deep Learning approach to solving a problem gives far better accuracy than other approaches. Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning. Therefore, every engineer, researcher, manager or scientist would be expected to know Machine Learning. There is massive growth in the machine learning and deep learning, and opportunities are skyrocketing, making this the perfect time to launch your career in this space. Please write back to us at reachus@cloudxlab.com or call us at 1 (412) 568-3901 (US) or 080 - 4920 2224 (IN) for more information.