Description

Buy Ritesh Kanjee – Machine Learning – Fun and Easy using Python and Keras Course at esyGB. You will have immediate access to the digital downloads in your account or your order email.

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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Ritesh Kanjee – 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