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• Deep Learning: A revolution in Artificial Intelligence Limitations of Machine Learning
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• Foundations for AI
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• Convolution Neural Networks
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• Recurrent Neural Networks
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• Deep Learning
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• Need for Data Scientists
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• Foundation of Data Science
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• What is Business Intelligence
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• What is Data Analysis
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• What is Data Mining
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• Foundations for ML
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• Clustering
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• Classification
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• Value Chain
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• Types of Analytics
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• Lifecycle Probability
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• Analytics Project Lifecycle
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• Advantage of Deep Learning over Machine learning
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• Reasons for Deep Learning
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• Real-Life use cases of Deep Learning
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• Review of Machine Learning
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• Basis of Data Categorization
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• Types of Data
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• Data Collection Types
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• Forms of Data & Sources
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• Data Quality & Changes
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• Data Quality Issues
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• Data Quality Story
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• What is Data Architecture
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• Components of Data Architecture
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• OLTP vs OLAP
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• How is Data Stored?
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• What is Big Data?
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• 5 Vs of Big Data
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• Big Data Architecture
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• Big Data Technologies
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• Big Data Challenge
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• Big Data Requirements
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• Big Data Distributed Computing & Complexity
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• Hadoop
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• Map Reduce Framework
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• Hadoop Ecosystem
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• What Data Science is
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• Why Data Scientists are in demand
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• What is a Data Product
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• The growing need for Data Science
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• Large Scale Analysis Cost vs Storage
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• Data Science Skills
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• Data Science Use Cases
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• Data Science Project Life Cycle & Stages
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• Data Acuqisition
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• Where to source data
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• Techniques
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• Evaluating input data
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• Data formats
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• Data Quantity
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• Data Quality
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• Resolution Techniques
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• Data Transformation
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• File format Conversions
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• Annonymization
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• Introduction
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• Clustering
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• Implementing Association rule mining
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• Decision Tree Classifier
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• Random Forest Classifier
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• Naive Bayes Classifier
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• Problem Statement and Analysis
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• Linear Regression
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• Logistic Regression
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• Support Vector Machines
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• Time Series Analysis
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• Machine Learning Project
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• Feature Selection and Pre-processing
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• Which Algorithms perform best
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• Model selection cross validation score
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• Text Mining& NLP
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• PySpark and MLLib
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• Deep Learning & AI
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• Introduction to Artificial Neural Networks
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• Convolutional Neural Networks
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• What are RNNs – Introduction to RNNs
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• Restricted Boltzmann Machine (RBM) and Autoencoders
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• Tensorflow with Python.
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