Data Scientist Real Interview Questions & Code Challenge

Course Curriculum

Chapter 1: Quick Brush – Deployment Architecture – Machine Learning & Deep Learning

Lecture 1: Course Introduction – About the course

Lecture 2: Architecture : Machine Learning Model Deployment in Production – with Tableau

Lecture 3: Machine Learning Model Deployment Architecture with Python Web Frameworks

Chapter 2: Quick Brush – Machine Learning – Frequently discussed @ Interview

Lecture 1: Machine Learning Process

Lecture 2: Algorithm : Density-based spatial clustering of applications with noise (DBSCAN)

Lecture 3: Algorithm – Introduction to FBProphet

Lecture 4: FBProphet Algorithm Documentation – Official Page

Lecture 5: Project Code : FBProphet Model Training

Lecture 6: Time Series Training & Validation Process

Lecture 7: Project Code : FBProphet Model Validation Accuracy, Forecasting & Decomposition

Lecture 8: Algorithm – Support Vector Machines (SVM) – Supervised Learning

Lecture 9: Algorithm – Support Vector Machines (SVM) Classifier – Supervised Learning

Lecture 10: Algorithm – Support Vector Machines (SVM) Regressor – Supervised Learning

Lecture 11: Algorithm – K-Nearest Neighbor KNN Classifier

Lecture 12: Algorithm – K-Nearest Neighbor KNN Regressor

Lecture 13: Algorithm – K – Means Clustering

Lecture 14: Algorithm – K-Means Clustering Elbow Plot Method

Lecture 15: Imputation Algorithm – Data Imputer with KNN-Imputer Algorithm

Lecture 16: Conda Cheat Sheets for Python Anaconda

Chapter 3: Quick Brush – Deep Learning , NLP & Computer Vision @ Interview Questions

Lecture 1: NLP Fuzzy Logic based on levenshtein distance for Sentence Similarity

Lecture 2: Code : NLP Fuzzy Logic based on levenshtein distance for Sentence Similarity

Lecture 3: Image Recognition – Convolutional Neural Network Architecture & Processing

Lecture 4: Code: Multiclass Keras Image Recognition Model Implementation

Lecture 5: Code – Image Recognition – Multi Class Object Detection with few lines of code

Lecture 6: Deep Learning Auto Encoder Models Benefits

Lecture 7: Deep Learning Encoders-Decoders – Auto Encoder Models Explanations

Lecture 8: Code: Keras Deep Learning Implementation for Auto Encoder Models

Lecture 9: Understanding Deep Learning Siamese Network

Lecture 10: Deep Learning Siamese Network Architecture

Lecture 11: Code : BERT Sentence NLP Transformer Embedding Generation

Lecture 12: NLP Universal Sentence Embedding with Tensorflow Deep Learning

Lecture 13: Code: NLP Text Clustering with Universal Sentence Embeddings

Lecture 14: Single Neuron Architecture & Processing

Lecture 15: Deep Neural Network Activation Functions

Lecture 16: Back Propagation & Chain Rule

Lecture 17: Global Minimum & Gradient Descent with Weight Optimization

Lecture 18: H2o.ai Automated Model Development with AutoML Frameworks

Chapter 4: Flask Web framework based API for Model Deployment

Lecture 1: Flask introduction

Lecture 2: Flask API Request and Response

Lecture 3: Flask File Upload

Lecture 4: Flask for Model deployment and prediction

Chapter 5: Quick Brush – Statistics – Frequently discussed @ Interview

Lecture 1: Statistics Hypothesis – Dickey Fuller Test

Lecture 2: Statistics Hypothesis – Shapiro Wilk Test

Lecture 3: Statistics Hypothesis – Mann-Whitney Test

Chapter 6: Surprise Gift -Personal Collection Real Machine Learning Interview Questions Set

Lecture 1: Data Scientist Interview Questions

Chapter 7: Join LIVE Mock Interview – SWOT analysis & Recommendation for Success

Lecture 1: Book your Live Interview

Chapter 8: Q & A – Machine Learning & Statistics – Frequently discussed @ Interview

Lecture 1: White Belt – Certified Machine Learning Professional

Lecture 2: Blue Belt – Certified Machine Learning Professional

Chapter 9: Q & A – NLP & Certified Deep Learning Professional

Lecture 1: Teach Machines to Speak – Natural Language Processing

Chapter 10: Code Challenge – Model Building Interview Exercise

Lecture 1: Kaggle Cloud Platform Introduction for Assessment Practice