• Post category:Udemy (Oct. 2021)
  • Reading time:7 mins read


Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python

What you will learn

Understanding the basic concepts

Complete tutorial about basic packages like Numpy and Pandas

Data Visualization

Data Preprocessing

Understanding the concept behind the algorithms

Developing different kinds of Machine Learning models

Knowing how to optimize your models’ hyperparameters

Learn how to develop models based on the requirement of your future business

Description

Are you interested in data science and machine learning, but you don’t have any background, and you find the concepts confusing?

Are you interested in programming in Python, but you always afraid of coding?

I think this course is for you!

Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.

This course is completely categorized, and we don’t start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:

Chapter1: Introduction and all required installations

Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)

Chapter3: Preprocessing


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Chapter4: Machine Learning Types

Chapter5: Supervised Learning: Classification

Chapter6: Supervised Learning: Regression

Chapter7: Unsupervised Learning: Clustering

Chapter8: Model Tuning

Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.

Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.

English
language

Content

Introduction
Course Content
What is Machine Learning? Some Basic Terms
Python Installation
Python IDE
IDE Installation
Installation of Required Libraries
Spyder Interface
Machine Learning Useful Packages (Libraries)
Python Source Codes
NumPy1
NumPy2
NumPy3
NumPy4
NumPy5
NumPy6
Pandas1
Pandas2
Pandas3
Pandas4
Visualization with Matplotlib1
Visualization with Matplotlib2
Visualization with Matplotlib3
Visualization with Matplotlib4
Visualization with Matplotlib5
Chapter 2 Quiz
Data Preprocessing
Reading and Modifying a Dataset
Statistics1
Statistics2
Statistics3 – Covariance
Missing Values1
Missing Values2
Outlier Detection1
Outlier Detection2
Outlier Detection3
Concatenation
Dummy Variable
Normalization
Chapter3 Quiz
Machine Learning Introduction
Learning Types
Chapter 4 Quiz
Supervised Learning – Classification
Supervised Learning Models – Introduction and Understanding the Data
k-NN Concepts
k-NN Model Development
k-NN Training-Set and Test-Set Creation
Decision Tree Concepts
Decision Tree Model Development
Decision Tree – Cross Validation
Naive Bayes Concepts
Naive Bayes Model Development
Logistic Regression Concepts
Logistic Regression Model Development
Model Evaluation Concepts
Model Evaluation – Calculating with Python
Chapter 5 Quiz
Supervised Learning – Regression
Simple and Multiple Linear Regression Concepts
Multiple Linear Regression – Model Development
Evaluation Metrics – Concepts
Evaluation Metrics – Implementation
Polynomial Linear Regression Concepts
Polynomial Linear Regression Model Development
Random Forest Concepts
Random Forest Model Development
Support Vector Regression Concepts
Support Vector Regression Model Development
Chapter 6 Quiz
Unsupervised Learning – Clustering Techniques
Introduction
K-means Concepts1
K-means Concepts2
K-means Model Development1
K-means Model Development2
K-means – Model Evaluation
DBSCAN Concepts
DBSCAN Model Development
Hierarchical Clustering Concepts
Hierarchical Clustering Model Development
Chapter 7 Quiz
Hyper Parameter Optimization (Model Tuning)
Introduction
Support Vector Regression – Model Tuning
K-Means – Model Tuning
k-NN – Model Tuning
Overfitting and Underfitting