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Machine Learning Crash Course for Executives - by Deloitte

 

Machine Learning Crash Course for Executives - by Deloitte

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Machine Learning Crash Course for Executives - by Deloitte Data Analytics, Data Analysis, Data Science, Big Data, Artificial Intelligence, Deep Learning, Neural Networks, AI
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What you'll learn

  • Machine Learning
  • Artificial Intelligence
  • AI
  • Deep Learning
  • Decision Trees
  • Ensemble Learning
  • Anomaly Detection
  • Clustering
  • Recommender Systems
  • Neural Networks
  • NLP
  • Natural Language Processing
  • LSTM
  • GRU
  • Data Science
  • Python
  • Keras
  • Gradient Boosting
  • Bagging
  • Random Forest
  • XGBoost
  • Logistic Regression
  • Regression
  • Classification
  • gradient
  • Model
  • Model Parameters
  • Computer Vision
  • Neural Networks
  • GAN
  • Unsupervised Learning
  • Supervised Learning
  • Natural Language Processing

Description

Deloitte's crash course on AI, Machine Learning and Deep Learning Programme is provides short, one stop learning opportunity for everybody that has an interest to understand AI, Machine Learning and Deep Learning beyond the buzzwords. After completing this course, participants will be able to prioritise, lead and manage AI initiatives. Deloitte developed this course according to following design principles:

  • One stop shop for AI, Machine Learning & Deep Learning
  • Short Learning Units - Microlearning
  • No prerequisites

Detailed Content of the Course:

MODULE 1: DEFINITIONS AND FUNDAMENTALS

  • LU 1.1: What is the difference between AI, Machine Learning and Deep Learning?
  • LU 1.2: What is the difference between Supervised, Unsupervised and Reinforcement Learning?
  • LU 1.3: How do machines learn?
  • LU 1.3.1: Loss Function and Mean Squared Error (MSE)
  • LU 1.3.2: Model Parameters
  • LU 1.3.3: Gradient Descent
  • LU 1.4: Regression and Classification
  • LU 1.4.1: Regression Case Study – Stock Price Prediction General Electric (GE)
  • LU 1.4.2: Classification
  • LU 1.4.2.1: Sigmoid Model & Logistic Regression
  • LU 1.4.2.2: How do we measure the performance of a classifier?
  • LU 1.4.2.3: Case Study Classification – Diabetes Prediction - Python, Pandas, Colab
  • LU 1.5: Machine Learning & Linear Algebra
  • LU 1.6: Underfitting, Overfitting and Regularization

MODULE 2: MACHINE LEARNING - CLASSIFIERS

  • LU 2.1: Decision Trees
  • LU 2.1.1: What are Decision Trees?
  • LU 2.1.2: Ensemble learning
  • LU 2.1.2.1: Bagging
  • LU 2.1.2.2: Boosting
  • LU 2.2: How to deploy a Machine Learning model in production
  • LU 2.2.1: Spam filter - Python & Visual Studio

MODULE 3: UNSUPERVISED LEARNING

  • LU 3.1: Anomaly Detection Systems
  • LU 3.1.1: Statistical Methods
  • LU 3.1.2: Density-based methods
  • LU 3.1.3: Isolation Forest
  • LU 3.2: Clustering
  • LU 3.3: Recommender Systems

MODULE 4: DEEP LEARNING

  • LU 4.1: History of Deep Learning
  • LU 4.1.1: The Perceptron
  • LU 4.1.2: From Perceptron to Neural Network
  • LU 4.1.3: Neural Networks and Deep Learning
  • LU 4.2: Anatomy of a Neural Network
  • LU 4.3: Backpropagation
  • LU 4.4: Regularization and Dropout
  • LU 4.5: Convolutional Neural Networks - Covnets - Computer Vision
  • LU 4.5.1: Why Covnets?
  • LU 4.5.2: What is Convolution?
  • LU 4.5.3: What is Pooling?
  • LU 4.5.4: What is Zero Padding?
  • LU 4.5.5: Covnet Architecture
  • LU 4.6: Dealing with text data
  • LU 4.6.1: One Hot Encoding
  • LU 4.6.2: Word Embeddings
  • LU 4.7: Recurrent Neural Networks
  • LU 4.7.1: LSTM and GRU - NLP - Natural Language Processing
  • LU 4.8: Generative Deep Learning – Everybody an Artist!
  • LU 4.8.1: Variational Autoencoders (VAEs)
  • LU 4.8.2: Generative Adversarial Networks (GANs)
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