Machine Learning  Fun and Easy using Python and Keras
Ritesh Kanjee, Masters Degree in Electronic Engineering


Section 1 Lecture 1  Introduction
2:14 
Section 2 Lecture 2  Download and Install Python Anaconda Distribution
9:23 
Section 2 Lecture 3 (Part 1)  "Hello World" in Jupyter Notebook
2:43 
Section 2 Lecture 3 (Part 2)  "Hello World" in Jupyter Notebook
12:00 
Section 2 Lecture 4  Installation for Mac Users
3:19 
Section 4 Lecture 7  Linear Regression  Theory
7:27 
Section 4 Lecture 8: Linear Regression  Practical Labs
10:13 
Section 5 Lecture 9  Decision Tree  Theory
8:20 
Section 5 Lecture 10  Decision Tree  Practical Labs
10:39 
Section 6 Lecture 11 Random Forest  Theory
7:14 
Section 6 Lecture 12: Random Forest Practical Labs
8:03 
Section 8 Lecture 14  Logistic Regression  Theory
7:43 
Section 8 Lecture 15  Logistic Regression Classification  Practical Labs
6:58 
Section 9 Lecture 16  K Nearest Neighbors  Theory
5:45 
Section 9 Lecture 17  KNN Classification  Practical Labs
6:46 
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 Lecture 21  Naive Bayes  Theory
11:40 
Section 11 Lecture 22  Naive Bayes  Practical Labs
6:05 
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
3:45 
Section 14 Lecture 27  Hierarchical Clustering  Theory
9:32 
Section 14 Lecture 28  Hierarchical clustering  Practical Labs
0:36 
Section 14 Lecture 29  Review Lecture
8:07 
Section 14 Lecture 31  Apriori
12:30 
Section 16 Lecture 32  Apriori  Practical Labs
8:23 
Sections 16 Lecture 33  Eclat  Theory
5:44 
Section 16 Lecture 34  Eclat Practical Labs
6:53 
Section 18 Lecture 36  Principal Component Analysis  Theory
12:48 
Section 18 Lecture 37  PCA  Practical Labs
3:21 
Section 19 Lecture 38  Linear Discriminant Analysis  Theory
7:40 
Section 19 Lecture 39  Linear Discriminant Analysis LDA  Practical Labs
5:17 
Section 21 Lecture 41  Artificial Neural Networks  Theory
18:30 
Section 21 Lecture 42  ANNperceptron  Practical Labs A
3:56 
Section 21 Lecture 43  ANN MLC  Practical Labs_C
2:57 
Section 21 Lecture 44  ANN MLC  Practical Labs_C
4:07 
Section 22 Lecture 45  Convolutional Neural Networks  Theory
11:18 
Section 22 Lecture 46  Convolution Neural Networks  Practical Labs
8:09 
Section 23 Lecture 47 Recurrent Neural Networks Theory
12:03 
Section 23 Lecture 48 Recurrent Neural Networks Practical Labs
5:25 
Section 24 Lecture 49  Conclusion
0:59

About This Class
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 subfield 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  KMeans, 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.
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, handson 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.
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