Ritesh Kanjee – Machine Learning – Fun and Easy using Python and Keras

Machine Learning presented to you in a simple and fun way along with Practical Labs using Python and Keras

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.

Excited Yet?

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.

Your Instructor

Ritesh Kanjee

I am the founder of Augmented Startups and I also hold a Masters Degree in Electronic Engineering. With over 63’000+ students on Augmented AI Bootcamp, and over 79’000 subscribers on YouTube, I teach the latest topics on Artificial Intelligence and Augmented Reality I will act as your mentor through helping you build or grow your expertise, we look forward to having you!

Course Curriculum

Machine Learning – Fun and Easy using Python and Keras
SECTION 1 – Introduction

Section 1 Lecture 1 – Introduction (2:14)

SECTION 2 – Setting up your Python Integrated Development Environment (IDE) for Course Labs

Section 2 Lecture 2 – Download and Install Python Anaconda Distribution (9:23)

Section 2 Lecture 3 – “Hello World” in Jupyter Notebook (16:22)

Section 2 Lecture 4 – Installation for Mac Users (3:19)

Section 2 Lecture 5 – Datasets, Python Notebooks and Scripts For the Course

SECTION 3 – Regression

Section 3 Lecture 6 – Regression

SECTION 4 – Linear Regression

Section 4 Lecture 7 – Linear Regression – Theory (7:26)

Section 4 Lecture 8 – Linear Regression – Practical Labs (10:13)

SECTION 5 – Decision Tree – Classification and Regression Trees

Section 5 Lecture 9 – Decision Tree – Theory (8:19)

Section 5 Lecture 10 – Decision Tree – Practical Labs (10:38)

SECTION 6 – Random Forests

Section 6 Lecture 11 – Random Forest – Theory (7:14)

Section 6 Lecture 12 – Random Forest Practical Labs (8:02)

SECTION 7 – Classification

Section 7 Lecture 13 – Classification

SECTION 8 – Logistic Regression

Section 8 Lecture 14 – Logistic Regression – Theory (7:43)

Section 8 Lecture 15 – Logistic Regression Classification – Practical Labs (6:57)

SECTION 9 – K Nearest Neighbors

Section 9 Lecture 16 – K -Nearest Neighbors – Theory (5:44)

Section 9 Lecture 17 – KNN Classification – Practical Labs (6:46)

SECTION 10 – Support Vector Machines (SVM)

Section 10 Lecture 18 – Support Vector Machine -Theory (7:27)

Section 10 Lecture 19 – Linear SVM – Practical Labs (2:54)

Section 10 Lecture 20 – Non Linear SVM – Practical Labs (1:53)

SECTION 11 – Naive Bayes

Section 11 Lecture 21 – Naive Bayes – Theory (11:39)

Section 11 Lecture 22 – Naive Bayes – Practical Labs (6:05)

SECTION 12 – Clustering

Section 12 Lecture 23 – Clustering

SECTION 13 – K – Means Clustering

Section 13 Lecture 24 – K – Means Clustering (8:42)

Section 13 Lecture 25 – K – Means Clustering – Practical Labs Part A (6:56)

Section 13 Lecture 26 – K – Means Clustering – Practical Labs Part B (3:44)

SECTION 14 – Hierarchical Clustering

Section 14 Lecture 27 – Hierarchical Clustering – Theory (9:32)

Section 14 Lecture 28 – Hierarchical clustering – Practical Labs (8:07)

Section 14 Lecture 29 – Review Lecture (0:35)

SECTION 15 – Associated Rule Learning

Section 15 Lecture 30 – Associated Rule Learning

SECTION 16 – Eclat and Apior

Section 16 Lecture 31 – Apriori (12:30)

Section 16 Lecture 32 – Apriori – Practical Labs (8:23)

Section 16 Lecture 33 – Eclat – Theory (5:44)

Section 16 Lecture 34 – Eclat Practical Labs (6:53)

SECTION 17 – Dimensionality Reduction

Section 17 Lecture 35 – Dimensionality Reduction

SECTION 18 – Principal Component Analysis

Section 18 Lecture 36 – Principal Component Analysis – Theory (12:48)

Section 18 Lecture 37 – PCA – Practical Labs (3:20)

SECTION 19 – Linear Discriminant Analysis LDA

Section 19 Lecture 38 – Linear Discriminant Analysis – Theory (7:40)

Section 19 Lecture 39 – Linear Discriminant Analysis LDA – Practical Labs (5:17)

SECTION 20 – Neural Networks

Section 20 Lecture 40 – Neural Networks

SECTION 21 – Artificial Neural Networks

Section 21 Lecture 41 – Artificial Neural Networks – Theory (18:30)

Section 21 Lecture 42 – ANN-perceptron – Practical Labs A (3:56)

Section 21 Lecture 43 – ANN Perceptron – Practical Labs_B (2:57)

Section 21 Lecture 44 – ANN MLC – Practical Labs_C (4:06)

SECTION 22 – Convolutional Neural Networks

Section 22 Lecture 45 – Convolutional Neural Networks – Theory (11:17)

Section 22 Lecture 46 – Convolution Neural Networks – Practical Labs (8:08)

SECTION 23 – Recurrent Neural Networks

Section 23 Lecture 47 – Recurrent Neural Networks – Theory (12:02)

Section 23 Lecture 48 – Recurrent Neural Networks – Practical Labs (5:25)

SECTION 24 – Conclusion and Bonus Section

Section 24 Lecture 49 – Conclusion (0:59)

Section 24 Lecture 50 – Little something for our Students

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