Do you want to learn Python Programming Language? Learn it from the Best Python Tutorial for Beginners, Certification, Course, and Training that you will find online. Python is a high level, general-purpose programming language. It is widely used by programmers all over the world. This object-oriented programming language has a large and comprehensive standard library. Python was first built in the 1980s and since then it has been developing. The latest version of this programming language, Python 3.0, was released in 2008. Ever since it was built, Python has been used by data scientists and programmers in every country. The best thing about Python is that it is easy to understand and adaptable with any of the operating systems. Anyone can learn Python programming language and use it to analyze data, create applications, develop web, and for many other things. It is the most in-demand programming language of this time. Python programmers get highly paid jobs for their skills. We have found the best courses you can find online to learn Python and listed those in here. These online courses will help you to shape your knowledge of Python. So, get through the list and details about those courses and chose one for yourself. Pierian Data International by Jose Portilla is presenting this online course on Python. You can go from the basics to creating your own applications and games with this course. It has a rating of 4.5 out of 5 on Udemy and over 457,000 enrolled students. This python tutorial for beginners provides 24 hours on-demand video, 19 articles and 19 coding exercises with lifetime access. This course will teach you both Python 2 and Python 3. You will learn to use Jupyter Notebook system and Object-Oriented Programming with online classes. This online course on Python programming language has over 100 lectures. It also includes quizzes, tests and homework assignments. They have 3 major projects to complete a Python portfolio.
Stanford University's Machine Learning Course Andrew Ng is the man. The founder of Google Brain and former chief scientist at Baidu, Andrew Ng's course is the clear winner in terms of ratings, reviews, and syllabus fit. Seeing how this course was what practically founded Coursera, that doesn't seem unbelievable. Although it has a smaller scope than the original Stanford class, it covers a large number of algorithms and techniques. The estimated timeline is eleven weeks, which includes two weeks of neural networks and deep learnings.
When the world's smartest companies such as Microsoft, Google, Alphabet Inc., and Baidu are investing heavily in Artificial Intelligence (AI), the world is going to sit up and take notice. Chinese Internet giant Baidu spent USD1.5 billion on research and development. And as proof of China's strong focus on AI and Machine Learning, Sinovation Ventures, a venture capital firm, invested USD0.1 billion in "25 AI-related startups" in the last three years in China and the U.S. Research shows that although genuine intelligence may still be a bit far off, AI and Machine Learning technologies are still expected to reign in 2017. Try reading up on Microsoft Project Oxford, IBM Watson, Google Deep Mind, and Baidu Minwa, and you'll understand what I am trying to get at. In 2015, Gartner's Hype Cycle for Emerging Technologies introduced Machine Learning (ML), and the graph showed (Figure 1) that it would reach a plateau in 2 to 5 years.
You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.
This book is available free in .PDF format via the link above, and the site offers links to all the lab code. Written by professors at USC, Stanford and the University of Washington and focused on R -- the language of statistical computing that is often used for machine learning and AI programs in this area -- the book has been described as "the'how to' manual for statistical learning." Once you're done with this book, move on to the authors' follow-up, " The Elements of Statistical Learning," also available for free online (although both can be purchased, as well).