Introduction to Datascience: Learn Julia Programming, Math & Datascience from Scratch

Description

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 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