Python for Machine Learning: The Complete Beginner’s Course

Python for Machine Learning: The Complete Beginner's Course

Python for Machine Learning: The Complete Beginner’s Course

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

Language: english

Note: 4.3/5 (517 notes) 63,266 students

Instructor(s): Meta Brains

Last update: 2022-05-26

What you’ll 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

 

Requirements

  • Experience with the basics of Python
  • Readiness, flexibility, and passion for learning
  • Basic mathematical skills

 

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!

 

Who this course is for

  • Anyone who want to pursue a career in Machine Learning
  • Any Python programming enthusiast willing to add machine learning proficiency to their portfolio
  • Technologists who are curious about how Machine Learning works in the real world
  • Programmers who are looking to add machine learning to their skillset

 

Course 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
  • Optional: Setting Up Python & ML Algorithms Implementation
    • Introduction
    • Python libraries for Machine Learning
    • Setting up Python
    • What is Jupyter?
    • Anaconda Installation Windows Mac and Ubuntu
    • Implementing Python in Jupyter
    • Managing Directories in Jupyter Notebook
  • 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

 

Python for Machine Learning: The Complete Beginner's CoursePython for Machine Learning: The Complete Beginner's Course

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