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• Machine Learning presented to you in a simple and fun way along with Practical Labs using Python and Keras Your InstructorRitesh KanjeeI 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 CurriculumMachine Learning – Fun and Easy using Python and KerasSECTION 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 CourseSECTION 3 – Regression Section 3 Lecture 6 – RegressionSECTION 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 – ClassificationSECTION 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 – ClusteringSECTION 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 LearningSECTION 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 ReductionSECTION 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 NetworksSECTION 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 StudentsAccess download Ritesh Kanjee – Machine Learning – Fun and Easy using Python and Keras at Forimc.com right now!Salepage: https://augmentedstartups.teachable.com/p/machine-learning-fun-and-easy-using-python-and-kerasArchive: https://archive.ph/wip/DwzAzDelivery Method– After your purchase, you’ll see a View your orders link which goes to the Downloads page. Here, you can download all the files associated with your order. – Downloads are available once your payment is confirmed, we’ll also send you a download notification email separate from any transaction notification emails you receive from esygb.com.
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