Machine Learning with Python from Scratch

Machine Learning with Python from Scratch, available at $54.99, has an average rating of 4.35, with 64 lectures, based on 329 reviews, and has 4622 subscribers.

You will learn about Have an understand of Machine Learning and how to apply it in your own programs Understand and be able to use Pythons main scientific libraries for Data analysis – Numpy, Pandas, Matplotlib and Seaborn. Understand and be able to use artificial neural networks Obtain a solid understand of machine learning in general Potential for a new job in the future. This course is ideal for individuals who are Students who wish to take their basic Python skills to the next level by mastering Pythons various scientific libraries or Students who want to understand and apply Machine Learning into their own programs or Students wanting to empower themselves with machine learning. It is particularly useful for Students who wish to take their basic Python skills to the next level by mastering Pythons various scientific libraries or Students who want to understand and apply Machine Learning into their own programs or Students wanting to empower themselves with machine learning.

Enroll now: Machine Learning with Python from Scratch

Summary

Title: Machine Learning with Python from Scratch

Price: $54.99

Average Rating: 4.35

Number of Lectures: 64

Number of Published Lectures: 64

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 64

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will Learn

Have an understand of Machine Learning and how to apply it in your own programs

Understand and be able to use Pythons main scientific libraries for Data analysis – Numpy, Pandas, Matplotlib and Seaborn.

Understand and be able to use artificial neural networks

Obtain a solid understand of machine learning in general

Potential for a new job in the future.

Who Should Attend

Students who wish to take their basic Python skills to the next level by mastering Pythons various scientific libraries

Students who want to understand and apply Machine Learning into their own programs

Students wanting to empower themselves with machine learning.

Target Audiences

Students who wish to take their basic Python skills to the next level by mastering Pythons various scientific libraries

Students who want to understand and apply Machine Learning into their own programs

Students wanting to empower themselves with machine learning.

Machine Learning is a hot topic!??Python Developers who understand how to work with Machine Learning are in high demand.

But how do you get started?

Maybe you tried to get started with Machine Learning, but couldn’t find decent tutorials online to bring you up to speed, fast.

Maybe the information you found was too basic, and didn’t give you the real-world Machine learning skills using Python that you needed.

Or maybe the information got bogged down in complex math explanations and was too difficult to relate to.

Whatever the reason, you are in the right place if you want to progress your skills in Machine Language using Python.

This course will help you to understand the main machine learning algorithms using Python, and how to apply them in your own projects.

But what exactly is Machine Learning?

It’s a field of computer science that gives computers the ability to “learn” – e.g. continually improve performance on a specific task, with data, without being explicitly programmed.

Why is it important?

Machine learning is often used to solve tasks considered too complex for humans to solve.??We create algorithms and apply a bunch of data to that algorithm and let the computer process (execute) the algorithm and search for a model (solution).

Because of the practical applications of machine learning, such as self driving cars (one example) there is huge interest from companies and government in Machine learning, and as a result, there are a a lot of opportunities for Python developers who are skilled in this field.

If you want to increase your career options, then understanding and being able to work with Machine Learning with your own Python programs should be high on your list of priorities.

What will you learn in this course?

For starters, you will learn about the main scientific libraries in Python for data analysis such as Numpy, Pandas, Matplotlib and Seaborn.

You’ll then learn about artificial neural networks and how to work with machine learning models using them.

You obtain a solid background in machine learning and be able to apply that knowledge directly in your own programs.

What are the Main topics included in the course?

Data Analysis with Numpy, Pandas, Matplotlib and Seaborn.

The machine learning schema.

Overfitting and Underfitting

K Fold Cross Validation

Classification metrics

Regularization:?Lasso, Ridge and ElasticNet

Logistic Regression

Support Vector Machines for Regression and Classification

Naive Bayes Classifier

Decision Trees and Random Forest

KNN classifier

Hyperparameter Optimization:?GridSearchCV

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

Kernel Principal Component Analysis (KPCA)

Ensemble methods: Bagging

AdaBoost

K means clustering analysis

Regression model and evaluation

Linear and Polynomial Regression

SVM, KNN, and Random Forest for Regression

RANSAC Regression

Neural Networks: Constructing our own MLP.

Perceptron and Multilayer Perceptron

And don’t worry if you do not understand some, or all of these terms. By the end of the course you will know what they are and how to use them.

Why enrolling in this course is the best decision you can make.

This course helps you to understand the difficult concepts of Machine learning in a unique way. Rather than just focusing on complex maths explanaitons, simpler explanations with charts, and info displays are included.

Many examples and genuinely useful code snippets are also included to make it even easier?to learn and understand.

After completing this course, you will have the necessary skills to apply Machine learning in your own projects.

The sooner you sign up for this course, the sooner you will have the skills and knowledge you need to increase your job or consulting opportunities. ? ?Your new job or consulting opportunity?awaits! ?

Why not get started today?

Click the?Signup?button to sign up for the course!

Course Curriculum

Chapter 1: Environment Setup

Lecture 1: L1-Anaconda

Lecture 2: L2-Jupyter Notebook

Chapter 2: Data Analysis

Lecture 1: L1-Introduction

Lecture 2: L2-Numpy: Array Concept and Math Operations

Lecture 3: L3-Numpy: Indexing, Slicing and Iterating

Lecture 4: L4-Numpy: Shape Manipulation

Lecture 5: L5-Numpy: Linear Algebra

Lecture 6: L6-Pandas: Data structures and properties

Lecture 7: L7-Pandas: Operations

Lecture 8: L8-Pandas: Applying Functions

Lecture 9: L9-Pandas: Importing and Exporting data

Lecture 10: L10-Pandas: Merge-Join-Concat-Group by

Lecture 11: L11-Pandas: Statistics with Pandas

Lecture 12: L12-Time Series with Pandas

Lecture 13: L13-Matplotlib basics

Lecture 14: L14-Matplotlib Subplots and Axes

Lecture 15: L15-Matplotlib: Object Oriented Method

Lecture 16: L16-Matplotlib: Color Maps

Lecture 17: L17-Matplotlib: Statistical Graphs part1

Lecture 18: L18-Matplotlib: Statistical Graphs part2

Lecture 19: L19-Seaborn: Basics

Lecture 20: L20-Seaborn: Color Palette

Lecture 21: L21-Seaborn: Categorical Data

Lecture 22: L22-Seaborn: Numerical Data

Chapter 3: Machine Learning

Lecture 1: L1-Introduction to Machine Learning

Lecture 2: L2-Overfitting and Underfitting

Lecture 3: L3-KFold Cross Validation

Lecture 4: L4-Classification Metrics

Lecture 5: L5-Logistic Regression

Lecture 6: L6-Plotting Decision Boundaries

Lecture 7: L7-Naive Bayes Classifier

Lecture 8: L8-Suppor Vector Machines for Classification

Lecture 9: L9-Decision Trees

Lecture 10: L10-Random Forest

Lecture 11: L11-KNN

Lecture 12: L12-GridSearchCV

Lecture 13: L13-K-Means

Lecture 14: L14-Principal Component Analysis(PCA)

Lecture 15: L15-Linear Discriminant Analysis(LDA)

Lecture 16: L16-Kernel Principal Component Analysis(KPCA)

Lecture 17: L17-Ensemble Methods(Bagging)

Lecture 18: L18-AdaBoost

Lecture 19: L19-Regression Model and Metrics

Lecture 20: L20-Linear Regression

Lecture 21: L21-Regularization with Lasso, Ridge and ElasticNet

Lecture 22: L22-Polynomial Regression

Lecture 23: L23-SVM, KNN and Random Forest for Regression

Lecture 24: L24-RANSAC Regression

Chapter 4: Neural Networks

Lecture 1: L1-Neural Networks Concepts-Part 1

Lecture 2: L2-Neural Networks Concepts-Part 2

Lecture 3: L3-Loss Functions

Lecture 4: L4-Activation Functions

Lecture 5: L5-Optimization of ANNs

Lecture 6: L6-Constructing an ANN with Python-part1

Lecture 7: L7-Constructing an ANN with Python-part2

Lecture 8: L8-Constructing an ANN with Python-part3

Lecture 9: L9-Perceptron with Scikit Learn

Lecture 10: L10-Multilayer Perceptron with Scikit Learn

Chapter 5: Applications

Lecture 1: L1-Datasets

Lecture 2: L2-ANN for Regression Part 1

Lecture 3: L3-ANN for Regression Part 2

Lecture 4: L4-Recognizing Handwritten Digits

Chapter 6: Extra Information – Source code, and other stuff

Lecture 1: Source Code

Lecture 2: Bonus Lecture and Information

Instructors

Tim Buchalkas Learn Programming Academy
Professional Programmers and Teachers – 2M students

CARLOS QUIROS
Industrial Engineer and Data Scientist

Rating Distribution

1 stars: 18 votes

2 stars: 16 votes

3 stars: 58 votes

4 stars: 131 votes

5 stars: 106 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!