Machine Learning Full Course for Beginners

Machine Learning Full Course for Beginners

Great Learning via YouTube Direct link

– What Is Machine learning? Introduction to Machine Learning

1 of 70

1 of 70

– What Is Machine learning? Introduction to Machine Learning

Class Central Classrooms beta

YouTube playlists curated by Class Central.

Classroom Contents

Machine Learning Full Course for Beginners

Automatically move to the next video in the Classroom when playback concludes

  1. 1 – What Is Machine learning? Introduction to Machine Learning
  2. 2 – Why Machine Learning?
  3. 3 – Road Map to Machine Learning
  4. 4 – How to Use Kaggle www.kaggle.com
  5. 5 - NumPy Python Tutorial How to Create NumPy Array
  6. 6 - How to Initialize NumPy Array
  7. 7 - How to check the shape of NumPy arrays
  8. 8 - How to Join NumPy Arrays
  9. 9 - NumPy Intersection & Difference
  10. 10 - NumPy Array Mathematics
  11. 11 - NumPy Matrix
  12. 12 - How to Transpose NumPy Matrix
  13. 13 - NumPy Matrix Multiplication
  14. 14 - NumPy Save & Load
  15. 15 - Python Pandas Tutorial
  16. 16 - Pandas Series Object
  17. 17 - Pandas Dataframe
  18. 18 - Matplotlib Python Tutorial
  19. 19 - Line plot
  20. 20 - Bar plot
  21. 21 - Scatter Plot
  22. 22 - Histogram
  23. 23 - Box Plot
  24. 24 - Violin Plot
  25. 25 - Pie Chart
  26. 26 - DoughNut Chart
  27. 27 - SeaBorn Line Plot
  28. 28 - SeaBorn Bar Plot
  29. 29 - SeaBorn ScatterPlot
  30. 30 - SeaBorn Histogram/Distplot
  31. 31 - SeaBorn JointPlot
  32. 32 - SeaBorn BoxPlot
  33. 33 – Role of Mathematics in Data Science
  34. 34 – What is data?
  35. 35 – What is Information?
  36. 36 – What is Statistics?
  37. 37 – What is Population?
  38. 38 – What is Sample?
  39. 39 – What are Parameters?
  40. 40 – Measures of Central Tendency
  41. 41 – Understanding Empirical Rule
  42. 42 – What is Mean, median, and mode?
  43. 43 – Measures of Spread Understanding Range, Inter Quartile Range & Box-plot
  44. 44 – Types of Machine Learning Supervised, Unsupervised & Reinforcement Learning
  45. 45 – How does a Machine Learning Model Learn?
  46. 46 – Supervised Machine Learning Mukesh Rao
  47. 47 – Python for Machine Learning
  48. 48 – Linear Regression Algorithm Hands-on
  49. 49 – What is Logistic Regression
  50. 50 – Linear Regression vs Logistic Regression
  51. 51 – Naïve Bayes Algorithm
  52. 52 – Diabetes Prediction using Naïve Bayes
  53. 53 – Decision Tree and Random Forest Algorithm
  54. 54 – Introduction to Support Vector Machines SVMs
  55. 55 – Kernel Functions
  56. 56 – Advantages & Disadvantages of SVMs
  57. 57 – K-NN Algorithm K-Nearest Neighbour Algorithm
  58. 58 – Introduction to Unsupervised Learning - Clustering
  59. 59 – Introduction to Principal Component Analysis
  60. 60 – PCA for Dimensionality Reduction
  61. 61 – Introduction to Hierarchical Clustering
  62. 62 – Types of Hierarchical Clustering
  63. 63 – How does Agglomerative hierarchical clustering work
  64. 64 – Euclidean Distance
  65. 65 – Manhattan Distance
  66. 66 – Minkowski Distance
  67. 67 – Jaccard Similarity Coefficient/Jaccard Index
  68. 68 – Cosine Similarity
  69. 69 – How to find an optimal number for clustering
  70. 70 – Applications Machine Learning

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.