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Category: Uncategorized Tags: Ritesh Kanjee, Ritesh Kanjee - Machine Learning - Fun and Easy using Python and Keras, Tags: Machine Learning - Fun and Easy using Python and Keras

Description

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

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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