Machine Learning with Python from Scratch
3.9 (257 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,891 students enrolled

Machine Learning with Python from Scratch

Mastering Machine Learning Algorithms including Neural Networks with Numpy, Pandas, Matplotlib, Seaborn and Scikit-Learn
3.9 (257 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,891 students enrolled
Last updated 7/2020
English [Auto]
Current price: $12.99 Original price: $79.99 Discount: 84% off
30-Day Money-Back Guarantee
This course includes
  • 12.5 hours on-demand video
  • 1 article
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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.
Course content
Expand all 64 lectures 12:38:24
+ Environment Setup
2 lectures 23:13

Get Python 3.6 distribution and scientific libraries from Anaconda

Preview 12:15

Get and basic use of Jupyter Notebook

L2-Jupyter Notebook
+ Data Analysis
22 lectures 04:34:18

Introduction to Data Analysis concept and tools

Preview 05:33

Review the array concept and perform math operations with Numpy

Preview 14:41

Learn the properties of indexing, slicing and iterating with Numpy

L3-Numpy: Indexing, Slicing and Iterating

Learn about array shape manipulation with Numpy

L4-Numpy: Shape Manipulation

Learn to perform linear algebra with Numpy

L5-Numpy: Linear Algebra

Learn about Pandas data structures and properties

L6-Pandas: Data structures and properties

Perform different operations with Pandas

L7-Pandas: Operations

Learn how to apply functions in Pandas dataframes

L8-Pandas: Applying Functions

Importing and Exporting data in Pandas

L9-Pandas: Importing and Exporting data

Perform different sql operations with pandas dataframes

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

Calculate some basic statistics using Pandas

L11-Pandas: Statistics with Pandas

Perform different operations for Time Series with Pandas

L12-Time Series with Pandas

Learn the basics to perform graphics in Matplotlib

L13-Matplotlib basics

Learn to create subplots in Matplotlib

L14-Matplotlib Subplots and Axes

The use of the Object Oriented Method in Matplotlib

L15-Matplotlib: Object Oriented Method

The use of Color Maps in Matplotlib

L16-Matplotlib: Color Maps

Applying Matplotlib for creating Statistical Graphs

L17-Matplotlib: Statistical Graphs part1

Applying Matplotlib for creating Statistical Graphs

L18-Matplotlib: Statistical Graphs part2

Starting with the basics in Seaborn

L19-Seaborn: Basics

The use of Color Palettes in Seaborn

L20-Seaborn: Color Palette

Plotting categorical data in Seaborn

L21-Seaborn: Categorical Data

Plotting numerical data in Seaborn

L22-Seaborn: Numerical Data
+ Machine Learning
24 lectures 04:48:30

Introduction to Machine Learning

L1-Introduction to Machine Learning

Learn concepts of Overfitting, Underfitting, Bias and Variance

L2-Overfitting and Underfitting

Learn the concept of KFold Cross Validation

L3-KFold Cross Validation

Learn how to perform metrics for classification models

L4-Classification Metrics

Learn the Logistic Regression model incluiding the multiclass classification

L5-Logistic Regression

A helper function to plot decision boundaries for any machine learning algorithm

L6-Plotting Decision Boundaries

Learn about Naive Bayes Classifier concept and code in python

L7-Naive Bayes Classifier

Learn about Support Vector Machines for classification

L8-Suppor Vector Machines for Classification

Learn about Decision Trees for classification

L9-Decision Trees

Learn about Random Forest for classification

L10-Random Forest

Learn about K-Nearest Neighborgs classifier


Optimizing models finding the best hyperparameters with GridSearchCV


Learn about unsupervised K-Means algorithm for classification


Learn how to use PCA to reduce the dimensionality of the data

L14-Principal Component Analysis(PCA)

Learn how to use LDA to reduce the dimensionality of the data

L15-Linear Discriminant Analysis(LDA)

Learn how to use KPCA to deal with non linear data

L16-Kernel Principal Component Analysis(KPCA)

Ensemble methods with Bagging

L17-Ensemble Methods(Bagging)

Ensemble methods with AdaBoost


Concepts of Regression model and metrics

L19-Regression Model and Metrics

Creating single and multiple linear regression models

L20-Linear Regression

Regularization models for Linear Regression: Lasso, Ridge and ElasticNet

L21-Regularization with Lasso, Ridge and ElasticNet

Polynomial Regression

L22-Polynomial Regression

Using SVM, KNN and Random Forest in Regression

L23-SVM, KNN and Random Forest for Regression

Regression with RANSAC

L24-RANSAC Regression
+ Neural Networks
10 lectures 01:56:29

Neural Networks Concepts-part 1

L1-Neural Networks Concepts-Part 1

Neural Networks Concepts-part 2

L2-Neural Networks Concepts-Part 2

Loss Functions in Neural Networks for classification and regression

L3-Loss Functions

Activation Functions in Neural Networks

L4-Activation Functions

Optimization parameters in ANNs

L5-Optimization of ANNs

Constructing an ANN with python-part1

L6-Constructing an ANN with Python-part1

Constructing an ANN with python-part2

L7-Constructing an ANN with Python-part2

Constructing an ANN with python-part3

L8-Constructing an ANN with Python-part3

Implementing a Perceptron model with Scikit Learn

L9-Perceptron with Scikit Learn

Implementing a Multilayer Perceptron with Scikit Learn

L10-Multilayer Perceptron with Scikit Learn
+ Applications
4 lectures 49:12

Refering two important sources of datasets with Kaggle and UCI ML repository


Simulation of a function creating a MLP for regression part 1

L2-ANN for Regression Part 1

Simulation of a function creating a MLP for regression part 2

L3-ANN for Regression Part 2

Recognizing Handwritten digits with different machine learning classifiers

L4-Recognizing Handwritten Digits
+ Extra Information - Source code, and other stuff
2 lectures 06:41
Source Code
Bonus Lecture and Information
  • Basic knowledge of Python
  • Basic knowledge of Linear Algebra
  • No previous experience in Machine learning, or any of the various libraries are needed.

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


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!

Who this course is for:
  • 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.