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Welcome to Introduction to Machine Learning Applications
NOTEBOOKS
1. Overview of Python Features
3. Introduction Datastructures (Varibles, Lists, Dictionaries, and Sets)
4. Overview of Numpy
5. Introduction to Pandas
6. Conditional Statements and Loops
7. Functions
8. Introduction to Apply Function
9. Null Values
10. Groupby and Pivot Tables
11. More Pivottables
12. Kaggle Baseline
13. Exercise 1
14. Feature Extraction
16. String Manipulation and Regular Expressions
17. Introduction to Seaborn
18. Web Mining
19. MatplotLab
20. Neural Networks and the Simplist XOR Problem
21. Train Test Splits
22. Classification with Scikit-learn
23. KNN
24. Titanic Classification
25. Titanic Classification - Challenge Solution
26. Titanic Classification - Titanic Visualize Decision Tree
27. Basic Text Feature Creation in Python
28. Titanic Regression
29. Titanic Standardization
30. Titanic PCA
32. Titanic Cluster
34. Linear Regression
35. Boston Housing
36. Lasso Ridge Regression
37. Regression with Stats-Models
38. Introduction to Principal Component Analysis
39. In Depth: Principal Component Analysis
40. k-Means Clustering
41. Coronavirus Data Modeling
42. Introduction to Text Mining in Python
43. Natural Language Toolkit
44. Bag-of-Words Using Scikit Learn
45. What’s Cooking in Python
46. Bag of Words
47. Sentiment Analysis
49. Lecture-21: Introduction to Natural Language Processing
51. IMDB
52. Introduction to Natural Language Processing
53. Vectorizors
55. Sample Coding Midterm.
56. Time Series Data
57. Panel Data vs Time Series Analysis
59. Time Series Analysis
60. MA, AR, and Arma
61. Out of Sample Prediction
62. Evaluation of Classifiers
63. Neural Networks
64. Tensorflow Introduction
67. Pytorch Tensors
68. Revisiting IRIS with PyTorch
69. Convolutional Neural Network with Pytorch
70. PyTorch Deep Explainer MNIST example
71. Pytorch Advantages vs Tensorflow
72. PyTorch Deep Explainer MNIST example
73. Revisting Boston Housing with Pytorch
74. Setup
75. Titanic Fastai
76. Ludwig
77. Transfer Learning - NLP
79. Titanic Classification - Deep Learning Tensorflow
81. Regression with Tensorflow/Keras
82. Tensorflow Graph Creation
ASSIGNMENTS
1. Assignment-1
2. [Fall 2022] Homework-2
16. Homework-3
27. Exam-1: Fall 2022
34. Homework-5
44. Homework-6
50. Homework-7
64. Exam-3: Fall 2022
Resources
Box Link
Hands On Machine Learning with Python
Dive into Deep Learning
Tensorflow Tutorials
The MS Business Analytics Capstone Course
Repository
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