## Machine Learning with Python – for Beginners

**Machine Learning with Python is a 10+ hours FREE course – a journey from zero to mastery.**

The course consist of the following content.

**15 video lessons**– which explain Machine Learning concepts, demonstrate models on real data, introduce projects and show a solution (YouTube playlist).**30 JuPyter Notebooks**– with the full code and explanation from the lectures and projects (GitHub).**15 projects**– with step guides to help you structure your solutions and solution explained in the end of video lessons (GitHub).

## TL;DR

**How to get started:**

**Download all Jupyter Notebooks**from repo (zip-file-download).**Unzip download**(main.zip) appropriate place.- Launch Ananconda and
**start JuPyter Notebook**(Install it from here if needed) **Open**the**first Notebook**from download.- Start watching the
**first video lesson**(YouTube).

## Who is Machine Learning with Python course for?

If you want to **learn Machine Learning **in a simple down to earth way.

You **don’t** **need** a strong background in **math**, **statistics**, **computer science **or any other high level degree.

All **you need** is a **desire** to learn **Machine Learning** and spend the time to **follow along **the 15 lessons in this course.

It would be good with some **Python fundamentals** – but don’t worry if don’t have that – there is a **FREE 8h Python beginners course **available here. It comes with a practical **eBook** with all you need to know and is structured in 17 lessons tailored to the course.

## What will you learn in the Machine Learning with Python course?

It will be an amazing journey from **zero experience** through all the important concepts in **Machine Learning **with **real life practical examples and projects** you will make together with me.

This includes the following.

*k*-Nearest-Neighbors Classifier**Linear Classifier****Support Vector Classification****Linear Regression****Reinforcement Learning****Unsupervised Learning****Neural Networks****Deep Neural Networks (DNN)****Convolutional Neural Networks (CNN)****PyTorch classifier****Recurrent Neural Networks (RNN)****Natural Language Processing****Text Categorization****Information Retrieval****Information Extraction**

Every concept is introduced with explanatory examples, with a in-depth project to play with it on your own afterwards.

Worried you cannot solve the problem. No worries – I will help you through the project in the end of the video tutorials.

## How to start?

- Download all the JuPyter Notebooks from my GitHub (you can get them as zip file here: download zip-file with full content).
- Don’t know what JuPyter Notebook is?
- Don’t worry – get the eBook from here or follow this course. The eBook explains how to get started.

- Don’t know what JuPyter Notebook is?
- Launch JuPyter Notebook.
- Don’t have JuPyter Notebook?
- Don’t worry – get it for FREE here: Download Anaconda
- Anaconda install JuPyter Notebook and Python and you will be ready to go.

- Don’t have JuPyter Notebook?
- Open the first JuPyter Notebook from the zip-file (or download from GitHub).
- Don’t know how to do that?
- Don’t worry – get the eBook from here or follow this course. The eBook explains how to get started with Anaconda, JuPyter Notebook and more.

- Don’t know how to do that?
- Start the first video on YouTube (YouTube).

# Lessons

## Lesson 00 – k-Nearest-Neighbors Classifier

In this first lesson you will learn about the following.

- What is the difference between
**classical computing**and**Machine Learning**. - How does
**Machine Learning work**. **Get**data,**prepare**data,**train**the model, and**test**the**model**.- The types of
**Machine Learning**:**Supervised learning, unsupervised learning**, and**reinforcement learning***(note: we will cover all in this course).* - In this lesson we learn about
**k-Nearest-Neighbors Classifier**– a supervised learning model. - We learn
**how to use it**. - We make a
**project**on real life**weather data**.

This will give you an **understanding** of what** Machine Learning is **and why it does **not** require **high level programmings skills** to master. Also, it will get you started with your first Machine Learning model – the **k-Nearest-Neighbors Classifier**.

Remember to get the JuPyter Notebooks used in the lecture from the GitHub. This way you will be able to follow along and make the project in the prepared JuPyter Notebooks.

## Lesson 01 – Linear Classifier

In this lesson we will explore the following.

- How
**Linear Classifier (supervised learning) works** - How are they different from
**k-Nearest-Neighbors Classifer**. - Understand the theory behind the
**Perceptron classifier**(the linear classifier) - How to
**prepare data**for the**model**(Perceptron classifier). **Visualize**the result of the**model**- Create a
**project**using the**Perceptron**classifier on real**weather data**.

This lesson will give you a broader understanding of what **Machine Learning **is, how the concepts are simple to understand and use. The next model (**Linear Classifier**) will be used to show **visually** how it **differs** from the previous one (**k-Nearest-Neighbor Classifier**).

Remember to get the JuPyter Notebooks used in the lecture from the GitHub. This way you will be able to follow along and make the project in the prepared JuPyter Notebooks.

## Lesson 02 – Support Vector Machines (SVM)

In this lesson we will continue with.

- Learn about the problem of
**seperation**. - The idea to
**maximize the distance**. - Work with examples to
**demonstrate**the issue. - Use the
**Support Vector Machine**(SVM) model on data. - Explore the result of
**SVM**on classification data. - Use the
**SVM**model in a project to classify dog species.

In this lesson you will learn about the **challenge** the find the **best fit** of a **Machine Learning model**. We will explore how the **Support Vector Machine** can help solve the problem of the optimal classification.

Remember to get the JuPyter Notebooks used in the lecture from the GitHub. This way you will be able to follow along and make the project in the prepared JuPyter Notebooks.

## Lesson 03 – Linear Regression

The goal of this lesson is.

- Learn about
**Linear Regression** - Understand
**difference**from**discrete classifier** - Understand it is
**supervised learning**task - Get insight into how
**similar**a**linear classifier**is to**discrete****classifier** **Hands-on**experience with**linear regression**

Here you will learn how to **predict** **precise** values using the **Linear Regression** model, more specifically, learn how to **predict house prices**. Also, you will explore some **common** **pitfalls**, which demonstrates the importance of understanding what the data represents.

Video lecture released on September 28 at 16:00 CET

## Lesson 04 – Reinforcement Learning

In this lesson we will do the following.

- Understand how
**Reinforcement Learning**works - Learn about
**Agent**and**Environment** - How it iterates and gets
**rewards based**on**action** - How to
**continuously learn**new things - Create own
**Reinforcement Learning**from scratch

The **Reinforcement Learning** model will teach you how simple the **Machine Learning** can be. You will create your own **model from scratch.** This will teach you **how to think **when creating Machine Learning models.

Video lecture released on October 5 at 16:00 CET

## Lesson 05 – Unsupervised Learning

Here we will explore and learn about.

- Understand how
**Unsupervised Learning**is different from**Supervised Learning** - How it can
**organize data without knowledge** **Understand**how**𝑘-Means Clustering**works**Train**a**𝑘-Means Cluster model**

Here you will learn how to **organize documents** with no prior knowledge and how to **optimize** the **parameters** of the algorithm **k-Means Clustering**.

Video lecture released on October 12 at 16:00 CET

## Lesson 06 – Neural Network

In this lesson we will learn the following.

- Understand
**Neural Networks** - How you can
**model**other**machine learning techniques** **Activation functions**- How to make simple
**OR function** - Different ways to
**calcualte weights** - Use
**tensorflow**with to build our model. - What
**Batch**sizes and**Epochs**are

You will learn about **Neural Networks** and how it works. It is an **essential** **building** **block** of modern Machine Learning.

Video lecture released on October 19 at 16:00 CET

## Lesson 07 – Deep Neural Network (DNN)

This lecture will cover.

- Understand
**Deep Neural Network**(DNN) - How algorithms
**calculate****weights**in**DNN**with**Backpropagation** - Show tools to
**visually understand**what**DNN**can solve - The problem of
**overfitting**models - How
**Dropout**works and use it. - Create our
**own****DNN**model - Explore a how to solve the
**XOR**–**problem**with**DNN**

This will teach you about **Deep Neural Networks** and demonstrate the **power** of this techniques. It will teach you how to solve problems, which are more **complex** than simple classification.

Video lecture released on October 26 at 16:00 CET

## Lesson 08 – Convolutional Neural Network (CNN)

Here we will explore the following.

- Understand what
**Convolutional Neural Network**(CNN) is - The
**strength**of**CNN** - How to use it to
**detect handwriting** - Extract
**features**from**pictures** - Learn
**Convolution, Pooling and Flatten** - How to create a
**CNN**to classify pictures of birds, airplanes and more.

**Convolutional Neural Network** (CNN) will teach you how to **classify images** – from **handwritten** **letters** to classification of **birds** and **airplanes**.

Video lecture released on November 2 at 16:00 CET

## Lesson 09 – PyTorch

In this lecture we will cover the following.

- What is
**PyTorch** **PyTorch**vs**Tensorflow**- Get started with
**PyTorch** - Work with
**image classification**with handwriting detection - Make a
**project**with detecting birds and airplanes pictures.

In this lesson you will learn how to use **PyTorch**, an alternative to **tensorflow**. You will learn to classify images with PyTorch using DNN.

Video lecture released on November 9 at 16:00 CET

## Lesson 10 – Recurrent Neural Network (RNN)

Here we will learn about.

- Understand
**Recurrent Neural Network**(RNN) - Build a
**RNN**on a**timeseries** - Hover over the
**theory**of**RNN**(**LSTM****cells**) - Use the
**MinMaxScaler**from**sklearn**. - Create a
**RNN****model**with**tensorflow** - Applying the
**Dropout**techniques. **Predict stock prices**and make**weather forecast**using**RNN**.

Here you will learn how to use **Recurrent Neural Network** (RNN), where you use data multiple times in the model. In this lesson you will learn how to use RNN on **timeseries** data to **predict** **stock prices** and **weather forecast.**

Video lecture released on November 16 at 16:00 CET

## Lesson 11 – Natural Language Processing

In this lesson we will learn the following.

- How the
**simple syntax**of**language**can be**parsed** - What
**Context-Free Grammar**(CFG) is - Use it to
**parse****text** - Understand
**word tokenization**of text and**trigrams** - See how it can be used to
**generate predictions** - Use the
**nltk****toolkit**. - A bit about
**Markov Chains**/models. - Show how to use
**markovify**library

You will learn the **limitations** of computers understanding of **language** as well as the **strengths**. How this knowledge can be used to create **models** for **natural** **language** **processing**.

Video lecture released on November 23 at 16:00 CET

## Lesson 12 – Text Categorization and Sentiment Classification

This lecture will teach you the following.

- What is
**Text Categorization** - Learn about the
**Bag-of-Words Model** - Understand
**Naive Bayes’ Rule** - How to use Naive Bayes’ Rule for
**sentiment classification**(text categorization) - What problem
**smoothing**solves

This will teach you how to** categorize documents** and get an understanding of the **sentiment** of the text. This is helpful in classifying whether a review is **positive** or **negative**.

Video lecture released on November 30 at 16:00 CET

## Lesson 13 – Information Retrieval

Here we will learn about the following.

- Learn what
**Information Retrival**is **Topic modeling**documents- How to use
**Term Frequenc**y and understand the limitations - Implement
**Term Frequency**by**Inverse Document Frequency**(TF-IDF) - This will teach how google engines can find the most
**relevant pages.** - Make our own
**TF-IDF calculation**to demonstrate the power.

You will learn how to find the most **significant words** in a **collection** of **document**. This teaches you how search engines like **Google** can find the most **relevant pages**.

Video lecture released on December 7 at 16:00 CET

## Lesson 14 – Information Extraction and Word2Vec

In this final lesson we will explore the following.

- What is
**Information Extraction** **Extract****knowledge**from patterns**Word****representation****Skip-Gram**architecture- To see how
**words**relate to each other (this is surprising) - How to use Word2Vec

This will teach you how** artificial intelligence** can get an **understanding** of **words** and get meaning out of it. This lecture will surprise you.

Video lecture released on December 14 at 16:00 CET