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
Variational Autoencoders
Requirements
No Prerequisites
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)

Who this course is for:
Executives curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Professionals curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Anybody with an interest in AI, Artificial Intelligence, Machine Learning and Deep Learning
IT professionals curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Directors curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Business Leaders curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
CEO curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Vice President curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Managers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Data Analist curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Beginner Python developers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Beginner Keras developers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning