English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3.5 Hours | 1.46 GB
Learn supervised learning for structured data, and implement them using Python programming
In this course, you will learn the basics of Machine Learning and Data Mining; almost everything you need to get started. You will understand what Big Data is and what Data Science and Data Analytics is. You will learn algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees, and Neural Networks. You’ll also understand how to combine algorithms into ensembles. Preprocessing data will be taught and you will understand how to clean your data, transform it, how to handle categorical features, and how to handle unbalanced data. By the end of this course, you will understand the ABCs of Data Mining and be able to implement what you’ve learnt on your own, more specifically, be able to implement what you’ve learnt on Python. There is no ideal student as there are no prior requirements needed – everybody is welcome!!
What Will I Learn?
- Understand Machine Learning, Data Mining, Big Data, Data Science, and Data Analytics
- Learn a little bit of coding in Python
- Learn Support Vector Machine, Linear Regression, Logistic Regression, K-Nearest Neighbor, Decision Trees, and Neural Networks
- Learn how to preprocess a dataset
- Learn how to handle categorical features
- Learn how to handle unbalanced datasets
- Understand the different validation methods
- Understand feature selection and dimensionality reduction
- Understand hyperparameter optimization
Table of Contents
1 Introduction and Course Scope
2 Big Data
3 Data Science
4 Data Analytics
5 Machine Learning and Data Mining
6 Machine Learning in This Course
7 Exploratory Data Analysis
8 Introduction to Python
9 Descriptive Statistics in Python
10 Dataset Resources
11 Linear Regression
12 Support Vector Machine
13 Support Vector Machine in Python
14 K-Nearest Neighbor
15 K-Nearest Neighbor in Python
16 Decision Trees
17 Decision Trees in Python
18 Logistic Regression
19 Neural Networks
20 Neural Networks in Python
21 Ensemble Learning
22 Ensemble Learning in Python
23 Regression Problem in Python
24 Performance Metrics
25 Overfitting vs Underfitting
26 Data Cleaning
27 Data Transformation
28 Data Transformation in Python
29 Categorical Features
30 Unbalanced Data
31 Validation Methods
32 The Holdout Method and Confusion Matrix in Python
33 The K Fold Method and Cleaning the Data in Python
34 Classifying New Observations
35 Feature Selection
36 Feature Selection in Python
37 Dimensionality Reduction
38 Principle Component Analysis in Python
39 Hyperparameter Optimization
40 Grid Search #1 in Python
41 Grid Search #2 in Python
42 Grid Search #3 in Python
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