Combat: classical algorithm and machine learning applications Python3 entry

• Chapter 1 Welcome to Python3 Machine Learning Fun

  Welcome to the "Python3 Fun machine learning" in the classroom. In this course, we start from zero, little by little, into the world of machine learning. This course of study in the field of machine learning, never more than just learning algorithms, further comprising adjusting such evaluation algorithm selection method, optimization models, parameters, organize data, and so on a series of work. Ready? Now our machine learning journey! ...

  •  1-1 What is Machine Learning Look
  •  Content and concepts covered in the course 1-2 Look
  •  1-3 Main courses stack used by Look

• Chapter 2 machine learning foundation

  Machine learning in the end is what the hell? This chapter will guide you in-depth understanding of machine learning in the world, so that we go to those familiar with the terminology may seem strange. Supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, batch learning, online learning, parameter learning, non-parametric study. After reading this chapter, you'll all understand these concepts it. Not only that, this chapter also includes philosophical study related quite profound and machine learning, let you think deeply about the machine learning ...

  •  2-1 machine learning data world
  •  The main task of machine learning 2-2
  •  2-3 supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning
  •  2-4 Batch learning, online learning, parametric and nonparametric learning learning
  •  2-5 and machine learning related to "philosophy" thinking
  •  2-6 courses built environment

• Chapter 3 Jupyter Notebook, numpy and matplotlib

  We must first of its profits. In this chapter, we will learn to use machine learning and related basic tools: Jupyter Notebook, numpy and matplotlib. Most tutorials explaining machine learning time, extensive use of these tools, these tools do not systematically explain. I deliberately added this chapter so that students in the process of writing the follow-up of machine learning algorithms, even easier! ...

  •  3-1 Jupyter Notebook basis
  •  3-2 Jupyter Notebook in command of magic
  •  3-3 Numpy data base
  •  3-4 Numpy create arrays (and matrix)
  •  3-5 Numpy array (and matrix) is the basic operation
  •  3-6 Numpy array (and matrix) combined with the division
  •  Matrix operation in 3-7 Numpy
  •  3-8 Numpy polymerization operation
  •  The operation 3-9 Numpy arg
  •  3-10 Numpy in comparison and Fancy Indexing
  •  3-11 Matplotlib data visualization base
  •  3-12 simple data loading and data exploration

   

  address