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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.
Course CurriculumTotal learning: 50 lessons Time: 5 hours
Section 1: Introduction
Section 2: Setting up your Python Integrated Development Environment (IDE) for Course Labs
Section 3: REGRESSION
Section 4: Linear Regression
Section 5: Decision Tree - Classification and Regression Trees
Section 6: Random Forests
Section 7: Classification
Section 8: Logistic Regression
Section 9: K nearest Neighbors
Section 10: Support Vector Machines (SVM)
Section 11: Naïve Bayes
Section 12: Clustering
Section 13: K – Means Clustering
Section 14: Hierarchical Clustering
Section 15: Association Rule Learning
Section 16: Eclat and Apior
Section 17: Dimensionality Reduction
Section 18: Principal Component Analysis
Section 19: Linear Discriminant Analysis LDA
Section 20: Neural Works
Section 21: Artificial Neural Networks
Section 22: Convolutional Neural Networks
Section 23: Recurrent Neural Networks
Section 24: Conclusion and Bonus Section