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 analysismodels 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.
What are the requirements?
- 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).
What am I going to get from this course?
- 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.
What is the target audience?
- 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.
I am the founder of Augmented Startups and I also hold a Masters Degree in Electronic Engineering. With over 43'000+ students on Augmented AI Bootcamp, and over 60'000 subscribers on YouTube, I teach the latest topics on Artificial Intelligence and Augmented Reality. Together with Geeky Bee AI, we will act as mentors through helping you build or grow your expertise, we look forward to having you!
PreviewSection 2 Lecture 2 - Download and Install Python Anaconda Distribution (9:23)
PreviewSection 2 Lecture 3 - "Hello World" in Jupyter Notebook (16:22)
PreviewSection 2 Lecture 4 - Installation for Mac Users (3:19)
PreviewSection 2 Lecture 5 - Datasets, Python Notebooks and Scripts For the Course