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    The Ultimate 15 Machine Learning Projects With Source Code

    15 Machine Learning Projects That Will Teach You All You Need as Machine Learning Authority

    As a beginner, you will spend time understanding the nature of Machine Learning. The best way to do that is to create many Machine Learning projects. For every Machine Learning project you do, another piece of the puzzle will fall into place.

    As a beginner, the big questions you have might be:

    • Is it difficult to become a Machine Learning expert?
    • Which Machine Learning projects to do?
    • Why will these Machine Learning projects be beneficial for your learning journey?
    • Why Machine Learning with Python is a great choice?
    • How to structure your Machine Learning projects?

    Or you can check if you need any of these Machine Learning projects with Python to extend your knowledge.

    Is Machine Learning Difficult?

    Machine Learning is the field of the following aspects.

    • Statistics and probability
    • Advanced mathematics like linear algebra
    • Programming

    Does that scare you?

    Machine Learning Models are created by experts, but users just need to learn to use them.

    You can learn to drive a car without being a car mechanic. The same is true for Machine Learning, you can use the models and get amazing results without building models from scratch.

    Using Machine Learning Models
    You don’t need to be a programming expert to use Machine Learning models.

    The programming aspect of using Machine Learning models is simple and on a high level, it does not require a lot from you. It boils down to knowing how to read data into data structures, then how to prepare the data for a model, this can include cleaning and a few more things, but it is not a difficult programming aspect. Finally, you need to know how to use the model, which is about making a query and using the result.

    To summarize your questions.

    • All Machine Learning models are ready to use. You don’t need to master advanced statistics, probability, or mathematics to use them. Some cool people have already done that for you.
    • The programming aspect is less complicated than you might think. You only need to be able to do a few things. On a high level you need the following: Read data, feed data to the model, and finally use the model.

    Learn through Machine Learning Projects

    Machine Learning Roadmap

    To become a Machine Learning authority you need to master a few steps.

    1. ML essentials. You need to understand what Machine Learning is and its fundamental building blocks. This will make it easier for you to learn the next steps and what they are used for. It simply answers the WHY and HOW we make Machine Learning projects.
    2. Programming basics. Machine Learning is structured through programming. The most commonly used language is Python, for a good reason, it is the easiest language to learn and has become the dominant language in the scientific communities.
    3. ML projects. Then you are ready to create a lot of awesome Machine Learning projects with Python. It is your basic learning cycle forward. Learn a new concept, create a project, play around with it, and repeat.

    In this article, you will learn all three steps.

    Machine Learning Essentials – What you need to know before starting your Machine Learning

    Classical programming vs Machine Learning

    Why is Machine Learning different from classical programming and what problems is it good for?

    Classical programming vs Machine Learning

    Classical programming

    • You, the programmer, need to figure out how to make instructions to the computer so it does what you want.
    • You need to cover all edge cases and be sure your logic is covering all options correctly.
    • Classical programming is good for simple problems, but not good for decisions.

    Machine Learning

    • You tell the model what you want and let it figure it out on its own.
    • You don’t need to handle edge cases, as the model will figure it out as it improves.
    • Machine Learning is good for decisions or other complex problems.

    Now you understand the essential difference of how traditional or classical programming differs from Machine Learning.

    How Machine Learning is Works

    On a high level, Machine Learning is divided into two phases.

    • Learning phase: Where the algorithm (model) learns in a training environment. When you support your child learning to ride the bike, like catching the child while falling not to hit too hard.
    • Prediction phase: Where the algorithm (model) is applied to real data. This is when the child can bike on her own.

    The three Types of Machine Learning

    Machine Learning is often categorized into three types.

    Types of Machine Learning

    A top-level understanding of the three types of Machine Learning.

    • Supervised Learning. You show examples of data you know and teach it by example. That is, you have data examples where you add the label it should have. Then you feed the model with data and labels. The model will then try to figure out how data can be used to say which label it belongs to.
    • Unsupervised Learning. Here you don’t know what you want with data at first. You feed the data to the model and it will try to find categories of data with similarities. This is great if you want to find new connections in data that you didn’t know existed.
    • Reinforcement Learning. You want to teach your model to behave in a specific manner. When it does what you want, you reward it. This is similar to training a dog, you do not talk the same language, but by rewarding the dog when it has the behavior you want, it learns them.

    Python Programming Basics for Machine Learning

    You already know that you do not need to be a master at programming and a great choice is Python language. Python is the chosen language in scientific circles and in Machine Learning.

    If you are new or need to refresh your Python programming skills a great place to start is this free course.

    • It covers all the Python programming basics you need.
    • It will even teach you to make a reinforcement Machine Learning model from scratch.
    • Everything is prepared for you to learn it easy with video instructions.

    15 Machine Learning Projects with code solutions

    The following 15 Machine Learning Projects with Python will teach you all aspects of Machine Learning that you need to know. After that, you can make your own Machine Learning specialization. The first step is to understand all aspects of it, that way you are well equipped to make good projects in the future.

    Project 1: k-Nearest-Neighbors Classifier

    Learn what Machine Learning is by comparing it to classical programming. Then how Machine Learning works and the three main categories of Machine Learning: SupervisedUnsupervised, and Reinforcement Learning.

    Finally, we will explore a Supervised Machine Learning model called k-Nearest-Neighbors(KNN) classifier to get an understanding through practical application.

    Goal of project.

    • Understand the difference between Classical Computing and Machine Learning
    • Know the 3 main categories of Machine Learning
    • Dive into Supervised Learning
    • Classification with 𝑘-Nearest-Neighbors Classifier (KNN)
    • How to classify data
    • What are the challenges with cleaning data
    • Create a project on real data with 𝑘-Nearest-Neighbor Classifier

    Project 2: Linear Classifier

    The goal is to learn about Supervised Learning and explore how to use it for classification. 

    Goal of project.

    • What is Supervised Learning
    • Understand the classification problem
    • What is the Perceptron classifier
    • How to use the Perceptron classifier as a linear classifier

    Project 3: Support-Vector Machine

    Understand the how Support-Vector Machine can help you classify data.

    Goal of project.

    • Learn about the problem of separation
    • The idea is 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.

    Project 4: Multiple Linear Regression

    Multiple Linear Regression is a Supervised learning task of learning a mapping from an input point to a continuous value.

    Goal of project.

    • Learn about Multiple Linear Regression
    • Understand the difference between discrete classifier
    • Understand it is a Supervised learning task
    • Get insight into how similar a linear classifier is to the discrete classifier
    • Hands-on experience with multiple linear regression

    Project 5: Reinforcement Learning

    Reinforcement Learning is like training a dog. You and the dog talk in different languages. This makes it difficult to explain to the dog what you want.

    Goal of project.

    • 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 your own Reinforcement Learning from scratch

    Project 6: Unsupervised Learning with k-Means Clustering

    Organize a set of objects into groups in such a way that similar objects tend to be in the same group. What is k-Means Clustering? Algorithm for clustering data based on repeatedly assigning points to clusters and updating those clusters’ centers.

    Goal of project.

    • 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

    Project 7: Artificial Neural Network

    Artificial Neural Networks are computing systems inspired by the biological neural networks that constitute animal brains. Often just called Neural Network.

    Goal of project.

    • Understand Neural Networks
    • How you can model other Machine Learning techniques
    • Activation functions
    • How to make simple OR function
    • Different ways to calculate weights
    • What Batch sizes and Epochs are

    Project 8: Deep Neural Network

    The adjective “deep” in deep learning refers to the use of multiple layers in the network. Usually having two or more hidden layers counts as deep.

    Goal of project.

    • Understand Deep Neural Networks (DNN)
    • How algorithms calculate weights in DNN
    • Show tools to visually understand what DNN can solve

    Project 9: Convolutional Neural Network

    This will teach you computational methods for analyzing and understanding digital images.

    Goal of project.

    • 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

    Project 10: PyTorch Model to Detect Handwriting

    PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. What does that mean? Well, PyTorch is an open-source machine learning library and is used for computer vision and natural language processing. It is primarily developed by Facebook’s AI Research Lab.

    Goal of project.

    • What is PyTorch
    • PyTorch vs Tensorflow
    • Get started with PyTorch
    • Work with image classification

    Project 11: Recurrent Neural Network (RNN) with Tensorflow

    Recurrent Neural Network is a Neural Network that generates output that feeds back into its own inputs. This enables it to do one-to-many and many-to-many relationships (not possible for feed-forward neural networks).

    Goal of project.

    • Understand Recurrent Neural Network (RNN)
    • Build an RNN on a time-series
    • Hover over the theory of RNN (LSTM cells)
    • Use the MinMaxScaler from sklearn.
    • Create an RNN model with TensorFlow
    • Applying the Dropout techniques.
    • Predict stock prices and make weather forecasts using RNN.

    Project 12: Natural Language Processing 

    NLP is the automatic computational processing of human language. This includes algorithms that take human written language as input and that produce natural text

    Goal of project.

    • How the simple syntax of language can be parsed
    • What Context-Free Grammar (CFG) is
    • Use it to parse text
    • Understand text in trigrams
    • A brief look at Markov Chains
    • See how it can be used to generate predictions

    Project 13: Sentiment Classification

    Text categorization (a.k.a. text classification) is the task of assigning predefined categories to free-text documents. It can provide conceptual views of document collections and has important applications in the real world.

    Goal of project.

    • 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

    Project 14: Term Frequency by Inverse Document Frequency

    The task of finding relevant documents in response to a user query. Web search engines are the most visible IR applications.

    Goal of project.

    • Learn what Information Retrieval is
    • Topic modeling documents
    • How to use Term Frequency and understand the limitations
    • Implement Term Frequency by Inverse Document Frequency (TF-IDF)

    Project 15: Extraction with Skip-Gram Architecture

    Information Extraction is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.

    Goal of project.

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

    Conclusion and Next Steps

    These 15 projects cover the essentials of Machine Learning. The best way to continue is in a similar manner. Most will find the sub-field of Machine Learning they like and continue to explore that area.

    Ask yourself the questions.

    • What did you find most interesting?
    • Are there aspects you didn’t really understand?
    • What projects would you like to make?

    Let the answers to these questions guide you.

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