I was emboldened to write this book after my video series called Data Science With Julia[1] got some traction. That too after a tweet about Decision Tree[2] was liked by Julia Language itself. So I thought why not give it more?
This book should be seen as my attempt to explain Data Science to my self and nothing more. Will this book rise to professional stature is yet to be seen
Author(s): Karthikeyan A K
Year: 2020
Language: English
Pages: 410
Preface
Front Cover
Back Cover
1. What you need to know
1.1. GNU/Linux
1.2. Math
2. What you need to have?
Datascience
3. What is Datascience?
4. Stages in Data Science
5. Predictive And Descriptive Analysis
6. Machine Learning, Artificial Intelligence and Data Science
Julia
7. Installing Julia
8. Julia REPL
9. Accessing Help
10. Package Management
11. Installing Jupyter notebook and Jupyter lab
12. Starting with Julia (using Jupyter) lab
13. Julia program in a file
14. Basic Arithmetic
15. Strings
16. Boolean Operations
17. Comparisons
18. Conditions and Branching
19. Ternary Operator
20. Short Circuit Evaluation
21. While Loops
22. Ranges and for loops
23. Breaks and Continues
24. Arrays
25. Tuples
26. Comprehension
27. Sets
28. Dictionaries
29. Comments
30. Functions
31. Regular Expressions (regexp)
32. Struct
33. Modules
34. Vectors & Matrix
35. Files
36. Scrapping
37. Plots
38. Dataframes
39. Debugging
Mathematics
40. Vectors
41. Matrices
42. Sigmoid
43. Bayesian
44. Statistics
45. Probability
Machine Learning
46. The Turing Test
47. Random Intelligence
48. GOFAI
49. Genetic Algorithms
50. K Nearest Neighbors
51. Decision Tree
52. Gradient Descent
53. Hot and Cold Learning
54. K Means Clustering
55. Naive Bayes For Text Classification
56. Perceptron Learning
57. Support Vector Machines (SVM)
58. Reinforcement Learning
Neural Networks
59. Brains of Animals
60. Artificial Neuron
61. Back propagation
Bibliography
