Machine Learning and Data Science with Python Online Course

Machine Learning and Data Science with Python

Artificial intelligence, machine learning, and deep learning neural networks are the most used terms in the technology world today. They’re also the most misunderstood and confused terms. Artificial intelligence is a broad spectrum of science which tries to make machines intelligent like humans, while machine learning and neural networks are two subsets that sit within this vast machine learning platform. But in this course, you will focus mainly on machine learning, which will include preparing your machine to make it ready for a prediction test.

You will be using Python as your programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has a variety of classes and features that perform complex mathematical analyses and provide solutions in just a few lines of code, making it easier for you to get up to speed with data science and machine learning.

Machine learning and data science jobs are among the most lucrative in the technology industry in recent times. Exploring this course will help you get well-versed with essential concepts and prepare you for a career in these fields.

Course Curriculum 

1. Introduction to Machine Learning
2. System and Environment preparation
3. Learn Basics of python
4. Learn Basics of NumPy
5. Learn Basics of Matplotlib
6. Learn Basics of Pandas
7. Understanding the CSV data file
8. Load and Read CSV data file
9. Dataset Summary
10. Dataset Visualization
11. Data Preparation
12. Feature Selection
13. Refresher Session - The Mechanism of Re-sampling, Training and Testing
14. Algorithm Evaluation Techniques
15. Algorithm Evaluation Metrics
16. Classification Algorithm Spot Check - Logistic Regression
17. Classification Algorithm Spot Check - Linear Discriminant Analysis
18. Classification Algorithm Spot Check - K-Nearest Neighbors
19. Classification Algorithm Spot Check - Naive Bayes
20. Classification Algorithm Spot Check – CART
21. Classification Algorithm Spot Check - Support Vector Machines
22. Regression Algorithm Spot Check - Linear Regression
23. Regression Algorithm Spot Check - Ridge Regression
24. Regression Algorithm Spot Check - LASSO Linear Regression
25. Regression Algorithm Spot Check - Elastic Net Regression
26. Regression Algorithm Spot Check - K-Nearest Neighbors
27. Regression Algorithm Spot Check – CART
28. Regression Algorithm Spot Check - Support Vector Machines (SVM)
29. Compare Algorithms - Part 1: Choosing the best Machine Learning Model
30. Compare Algorithms - Part 2: Choosing the best Machine Learning Model
31. Pipelines: Data Preparation and Data Modelling
32. Pipelines: Feature Selection and Data Modelling
33. Performance Improvement: Ensembles – Voting
34. Performance Improvement: Ensembles – Bagging
35. Performance Improvement: Ensembles – Boosting
36. Performance Improvement: Parameter Tuning using Grid Search
37. Performance Improvement: Parameter Tuning using Random Search
38. Export, Save and Load Machine Learning Models: Pickle
39. Export, Save and Load Machine Learning Models: Joblib
40. Export, Save and Load Machine Learning Models Joblib
41. Finalizing a Model - Introduction and Steps
42. Finalizing a Classification Model - The Pima Indian Diabetes Dataset
43. Quick Session: Imbalanced Data Set - Issue Overview and Steps
44. Iris Dataset: Finalizing Multi-Class Dataset
45. Finalizing a Regression Model - The Boston Housing Price Dataset
46. Real-time Predictions: Using the Pima Indian Diabetes Classification Model
47. Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
48. Real-time Predictions: Using the Boston Housing Regression Model