Real World Machine Learning By Building 10 Projects
We are very excited to start work on our new course on Machine Learning. A course where you will learn to implement cutting edge machine learning algorithms to solve real world problems. We have carefully selected the projects which will cover important aspect of Machine learning such as Supervised Learning, Unsupervised learning and Neural network with deep learning. You will start with real world data available publicly to create these Machine Learning's Projects. It will be a course for serious developers but will be fun and engaging. You will learn step by step implementation and can be a professional ML developer after completing this course.
What is Machine Learning?
Machine Learning as a career
Machine learning is the top emerging Job vertical among all technology jobs world wide.
With new avenues of machine learning such as consumer behaviour analysis, marketing and sales forecasting, IT security, fraud detection , finance and office automation there is a big opportunity for trained Machine learning experts. Our course aims to provide expertise which you can directly transfer in your day to day Jobs.
Machine Learning’s Potential To Change The World
Machine Learning offers a slew of different opportunities in multiple sectors including Business, Medicine, Marketing, etc. From credit card fraud detection to offering a more personalized marketing experience, machine learning can not only help but will also change the way we surf the web, drive our cars and maybe even shower.
Machine Learning is currently one of the hottest trends on the market with companies big and small using learning algorithms to offer a more personalised user experience for their customers.
What This Course Offers
Machine Learning comes with a lot of potential and it doesn’t seem to be stopping any time soon. As technology gets smarter and better, so will your algorithms. If you’ve always wanted to help build a more futuristic world, then this is your chance. We have designed the perfect course for helping you get started with machine learning by helping you understand the key algorithms that are used when writing machine learning programs.
This course won’t focus on the theory or the boring aspects of machine learning, but instead you will actually build your own apps. You will learn how to write the codes and then see them in action. Using 5 projects (Now 10 projects) that range from simple to more complex ones, you’ll actually learn how to think like a machine learning expert.
Why This Course?
This course has been designed keeping in mind all types of learners. It includes something for everyone, from newbies to even advanced programmers. The course focuses on helping you actually learn how to code and write the algorithms, instead of simply focusing on the theoretical or the boring aspects of Machine Learning.
The syllabus has been curated by experts and dedicates itself to bringing you the best approach approach to ML. The course has an example-based approach and will help you become more comfortable applying algorithms to real world problems. The course will focus on building 10 projects from scratch that will not only familiarize you with ML algorithms, but also will help you learn exactly how to use them.
Tentative 10 Projects
First 5 New Projects are the additional projects which will be Added to the Course as we reached our First stretch goal.
Project 1 – Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences
In this tutorial, we will explore the world of bioinformatics by using Markov models and K-nearest neighbor (KNN) algorithms to classify E. Coli DNA sequences. This project will use a dataset from the UCI Machine Learning Repository that has 106 DNA sequences, with 57 sequential nucleotides (“base-pairs”) each.
Project 2 – Getting Started with Natural Language Processing In Python
Here we will cover the basics of Natural Language Processing (NLP) methodology, including tokenizing words and sentences, part of speech identification and tagging, and phrase chunking. After this project, the student should have the necessary foundation to begin building and deploying machine learning algorithms for natural language processing.
Project 3 – Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning
Using the CIFAR-10 object recognition dataset as a benchmark, we will implement a recently published deep neural network that can obtain similar results to state-of-the-art networks, despite having less parameters and smaller computational requirements.
Project 4 – Image Super Resolution with the SRCNN
In this tutorial, we will implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) to improve the image quality of degraded images.
Project 5 – Natural Language Processing: Text Classification
Building on the foundation developed in the previous project, this tutorial will dive deeper into Natural Language Processing. We will solve a text classification task using multiple classification algorithms, including a Naïve Bayes classifier, SGD classifier, and linear support vector classifier (SVC).
Project 6 – Stock Market Clustering Project
This project will use a K-means clustering algorithm to identify related companies by finding correlations among stock market movements over a given time span. It will use the open source data from the Yahoo Finance Python module. Also, in this project you are going to do a PCA dimensionality reduction to plot the data on a 2D plot.
Project 7 – Breast Cancer Detection
This second project will build a program to help detect breast cancer malignancies by using a support vector machine. You are also going to use the K-nearest neighbour algorithm to compare and contrast performance with the support vector machine.
Project 8 – Board Game Review
This project is actually going to be predicting board game reviews and in this project you are going to predict the average reviews on a board game based on characteristics such as difficulty, length, number of players. This will be accomplished by performing linear regression analysis.
Project 9 – Credit Card Fraud Detection
In this project, you are going to do a credit card fraud detection and going to focus on anomaly detection by using probability densities.
Project 10 – Diabetes Onset Detection
This final project is going to be a diabetes onset detection with deep learning grid search. In this project, you will fine-tune a deep learning neural network by performing a grid search. The network will be used to detect the onset of diabetes based on patient data.
About the Company - Eduonix Learning Solutions
Eduonix learning Solutions is the premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content.
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Risks and challenges
We have successfully delivered all our previous 8 projects on time, so we run a very small risk that we might be able to finish the course in the stipulated time. However, with multiple people working on this project day and night, we are confident that we will not pass the deadline.Learn about accountability on Kickstarter
- (30 days)