The Fun and Easy Guide to Machine Learning using Keras
What you'll learn
- You will learn the fundamentals of the main Machine Learning Algorithms and how they work on an Intuitive level.
- We teach you these algorithms without boring you with the complex mathematics and equations.
- You will learn how to implement these algorithms in Python using sklearn and numpy.
- You will learn how to implement neural networks using the h2o package
- You will learn to implement some of the most common Deep Learning algorithms in Keras
- Build an arsenal of powerful Machine Learning models and how to use them to solve any problem.
- You will learn to Automate Manual Data Analysis Tasks.
- PC/ Laptop to implement the Practical Labs, running Windows or Mac.
- High school knowledge in mathematics.
- Willingness to Learn and Open Mind.
- Background in engineering, data science, computer science and statistics is recommended (but not a requirement)
- Basic Python or Programming Background recommended (but not a requirement).
Welcome to the Fun and Easy Machine learning Course in Python and Keras.
Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.
So Many Machine Learning Courses Out There, Why This One?
This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package.
We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.
What you will Learn in this Course
This is how the course is structured:
Regression – Linear Regression, Decision Trees, Random Forest Regression,
Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,
Clustering - K-Means, Hierarchical Clustering,
Association Rule Learning - Apriori, Eclat,
Dimensionality Reduction - Principle Component Analysis, Linear Discriminant Analysis,
Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.
Practical Lab Structure
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network (CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers.
So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your machine learning abilities.
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.
TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course.
Who this course is for:
- Student who starting out or interested in Machine Learning or Deep Learning.
- Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning
- Students interested in gaining exposure to the Keras library for Deep Learning.
- Data analysts who want to expand into Machine Learning.
- College students who want to start a career in Data Science.
So a bit about me, Ritesh Kanjee: I've graduated from University of Johannesburg as an Electronic Engineer with a Masters in Image Processing and 8 years ago I started my online school called Augmented Startups where I have over 100'000 subscribers on YouTube and over 60'000 students on Augmented AI Bootcamp/Udemy.
I’ve worked with popular tools such as TensorFlow Keras, Open CV, and PyTorch and I’ve also produced High ranking tutorials that feature on Google and YouTube. My Machine Learning Series is also one of the most viewed videos, over 300 thousand views and you’ll find them ranked right at the top on YouTube search results.
From my tutorials, I have received a lot of great feedback and testimonials from students all around the world, I will share those reviews towards the end of the video
And I have also presented at international conferences and meetups in AI. For industry standard AI, I have partnered up with Geeky Bee AI who are Experts in the field in AI and Deep Learning and have experience developing AI apps for real world applications.
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).