Python for Machine Learning: The Complete Beginner's Course

Learn to create machine learning algorithms in Python for students and professionals

4.48 (357 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Python for Machine Learning: The Complete Beginner's Course
53,067
students
2.5 hours
content
May 2022
last update
$64.99
regular price

What you will learn

Learn Python programming and Scikit learn applied to machine learning regression

Understand the underlying theory behind simple and multiple linear regression techniques

Learn to solve regression problems (linear regression and logistic regression)

Learn the theory and the practical implementation of logistic regression using sklearn

Learn the mathematics behind decision trees

Learn about the different algorithms for clustering

Description

To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! 

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.

That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you've gotten your hands on the code, you'll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context. 

You'll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!

Content

Introduction to Machine Learning

What is Machine Learning?
Applications of Machine Learning
Machine learning Methods
What is Supervised learning?
What is Unsupervised learning?
Supervised learning vs Unsupervised learning
Course Materials

Simple Linear Regression

Introduction to regression
How Does Linear Regression Work?
Line representation
Implementation in python: Importing libraries & datasets
Implementation in python: Distribution of the data
Implementation in python: Creating a linear regression object

Multiple Linear Regression

Understanding Multiple linear regression
Implementation in python: Exploring the dataset
Implementation in python: Encoding Categorical Data
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Training the model on the Training set
Implementation in python: Predicting the Test Set results
Evaluating the performance of the regression model
Root Mean Squared Error in Python

Classification Algorithms: K-Nearest Neighbors

Introduction to classification
K-Nearest Neighbors algorithm
Example of KNN
K-Nearest Neighbours (KNN) using python
Implementation in python: Importing required libraries
Implementation in python: Importing the dataset
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Feature Scaling
Implementation in python: Importing the KNN classifier
Implementation in python: Results prediction & Confusion matrix

Classification Algorithms: Decision Tree

Introduction to decision trees
What is Entropy?
Exploring the dataset
Decision tree structure
Implementation in python: Importing libraries & datasets
Implementation in python: Encoding Categorical Data
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Results prediction & Accuracy

Classification Algorithms: Logistic regression

Introduction
Implementation steps
Implementation in python: Importing libraries & datasets
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Pre-processing
Implementation in python: Training the model
Implementation in python: Results prediction & Confusion matrix
Logistic Regression vs Linear Regression

Clustering

Introduction to clustering
Use cases
K-Means Clustering Algorithm
Elbow method
Steps of the Elbow method
Implementation in python
Hierarchical clustering
Density-based clustering
Implementation of k-means clustering in python
Importing the dataset
Visualizing the dataset
Defining the classifier
3D Visualization of the clusters
3D Visualization of the predicted values
Number of predicted clusters

Recommender System

Introduction
Collaborative Filtering in Recommender Systems
Content-based Recommender System
Implementation in python: Importing libraries & datasets
Merging datasets into one dataframe
Sorting by title and rating
Histogram showing number of ratings
Frequency distribution
Jointplot of the ratings and number of ratings
Data pre-processing
Sorting the most-rated movies
Grabbing the ratings for two movies
Correlation between the most-rated movies
Sorting the data by correlation
Filtering out movies
Sorting values
Repeating the process for another movie
Quiz Time

Conclusion

Conclusion

Reviews

Nayibe
June 27, 2022
Es el mejor curso que he visto hasta el momento, he entendido cosas que no había tenido oportunidad de entender jamás. Lo recomiendo totalmente.

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4396628
udemy ID
11/14/2021
course created date
3/19/2022
course indexed date
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