The Base Camp for your Ascent in Machine Learning

Explanation of the localhost and its applications.

Explanation of Business Intelligence and listing of BI tools.

Explanation of the standard deviation and the relationship to the variance.

Explanation of a residual neural network with example in TensorFlow.

Explanation of the Curse of Dimensionality and its problems.

Explanation of selection bias with examples.

Explanation of OneDrive and the cost structure.

Explanation of tSNE including example in Python.

Explanation of Batch Normalization and its advantages.

Explanation of the XGBoost library including the gradient boosting method.

Explanation of tasks and training of a DevOps Engineer.

Explanation of Continuous Integration with advantages and disadvantages.

Introduction to Git and useful terms

Apache Airflow explained with architecture and application examples.

Explanation of different types of code repositories.

Structure of Apache Kafka explained with possible fields of application.

Introduction to Bitbucket, its features and pricing model.

Explanation of Algorithms with examples and reference to Machine Learning.

Description of the star scheme compared to the snowflake scheme.

Explanation of Apache Spark with a comparison to Hadoop.

Explanation of database schemas by example.

Explanation of DevOps with benefits and possible tools.

Explanation of On-Premises versus Cloud.

Explanation of perceptrons with example and how neural networks arise from them.

Explanation of cloud types and different service models.