English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3.5 Hours | 1.58 GB
This training is an introduction to the concept of machine learning, its algorithms and application using Python.
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.
This training is an introduction to the concept of machine learning, its algorithms and application using Python.
The training will include the following;
- What is Machine Learning? (Intro – why its used, Data Science defined)
- Analytics Defined (Predictive, Prescriptive etc.,)
- Data Mining Flow(Phases defined – with MOdeling phase that involves ML)
- Explanation on Data Set
- Supervised Learning
- Unsupervised Learning
- Classification Algorithms
- Regression Algorithms
- Linear Regression
- Logistic Regression
- Naive Bayes Classifier
- Anonymous Detection
- Decision Trees
- Random Forest
- Neural Networks
- K-Means Clustering
- Apriori algorithm
- Feature Selection
- Support Ventor Machine
- Basic explanation on Use Cases
- Basic Functions defines (Cost function, likelihood function, normalization, trade off etc.,)
- Primary tools/ Softwares used for ML
- Python Packages for Machine Learning
Table of Contents
Introduction
1 Introduction Machine Learning Using Python
Usage of Machine Learning Packages in Python
2 Installation of Python
3 Example of Machine Learning Using Python
4 Example of Machine Learning Using Python Continues
Linear Regression
5 Linear Regression in ML
6 Linear Regression Example
7 Linear Regression Example Continues
8 Support Vector Algorithm in ML
Classifier and Python Package
9 Decision Tree Classifier
10 Random Forest Classification
11 K Mean Clustering
12 Apriori Python Package
13 Apriori Python Package Continues
Evaluation Metrics
14 Evaluation Metrics
15 Example of Evaluation Metrics
16 Confusion Matix in Evaluation Metrics
17 Classification Reports in Evaluation Metrics
18 Example of MAE, MSE and Variance using Evaluation Metrics
19 Sea Born Example using Evaluation Metrics
20 Scatter Matrix using Evaluation Metrics
Missing Value
21 Handling Missing Values in Python
22 Handling Missing Values in Python Continues
23 Exception Handling in Python
24 More on Exception Handling in Python
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