How to utilise Appliedaicourse 0/1
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Python for Data Science Introduction 0/1
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Python for Data Science: Data Structures 0/6
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Python for Data Science: Functions 0/10
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Python for Data Science: Numpy 0/2
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Python for Data Science: Matplotlib 0/1
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Python for Data Science: Pandas 0/3
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Python for Data Science: Computational Complexity 0/4
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Plotting for exploratory data analysis (EDA) 0/0
exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
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Linear Algebra 0/1
It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms.
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Probability and Statistics 0/32
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Interview Questions on Probability and statistics 0/0
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Dimensionality reduction and Visualization: 0/0
In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
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PCA(principal component analysis) 0/0
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(t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/1
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Interview Questions on Dimensionality Reduction 0/0
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Real world problem: Predict rating given product reviews on Amazon 0/17
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Classification And Regression Models: K-Nearest Neighbors 0/32
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Interview Questions on K-NN(K Nearest Neighbour) 0/0
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Classification algorithms in various situations 0/21
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Performance measurement of models 0/10
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Interview Questions on Performance Measurement Models 0/0
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Naive Bayes 0/22
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Logistic Regression 0/18
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Linear Regression 0/4
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Solving Optimization Problems 0/12
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Interview Questions on Logistic Regression and Linear Regression 0/0
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Support Vector Machines (SVM) 0/16
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Interview Questions on Support Vector Machine 0/0
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Decision Trees 0/16
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Interview Questions on decision Trees 0/0
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Ensemble Models 0/20
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Featurization and Feature engineering. 0/18
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Miscellaneous Topics 0/10
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Unsupervised learning/Clustering 0/14
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Hierarchical clustering Technique 0/7
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DBSCAN (Density based clustering) Technique 0/11
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Recommender Systems and Matrix Factorization 0/16
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Interview Questions on Recommender Systems and Matrix Factorization. 0/0
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Case study 2: Stackoverflow tag predictor 0/17
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Case Study 3: Quora question Pair Similarity Problem 0/16
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Case Study 4: Amazon fashion discovery engine 0/27
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Case Study 5: Microsoft Malware Detection 0/20
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Case study 6:Netflix Movie Recommendation System 0/27
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Case Study 7: Personalized Cancer Diagnosis 0/21
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Case study 8:Taxi demand prediction in New York City 0/28
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Deep Learning:Neural Networks. 0/14
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Deep Learning: Deep Multi-layer perceptrons 0/21
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Deep Learning: Tensorflow and Keras. 0/14
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Deep Learning: Convolutional Neural Nets. 0/19
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Deep Learning: Long Short-term memory (LSTMs) 0/11
- Lecture51.1
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Interview Questions on Deep Learning 0/1
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Case Study 9: Self Driving Car 0/13
- Lecture53.1
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Case Study 10: Music Generation using Deep-Learning 0/11
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Case Study 11: Human Activity Recognition 0/8
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Facebook Friend Recommendation using Graph Mining 0/19
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SQL 0/27
- Lecture57.1
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Case Studies 0/1
- Lecture58.1
Interview Questions 0/2
- Lecture59.1
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- Lecture59.3
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