World No. 1 Data Science and AI in Healthcare Training Course

Online/ Virtual Training

1 - 4 March, 2021

AI Roadmap in Healthcare

Roadmap to Machine Learning

Led by:

Walid Semaan

Founder and President

Matrix TRC "Data Science and AI Academy

Course description:

This 4-day (3h per day) online live course is intendent to go through the complete roadmap that leads to the immense universe of Artificial Intelligence. It starts from ground zero, a quick overview on basic statistics and data analysis, pillars upon which both “supervised” and “unsupervised” Machine Learning evolved and became at their turn the inevitable backbone for all Artificial Intelligence algorithms behind successful AI applications.

In parallel to dozens of step-by-step case studies, the workshop will explore simultaneously, and under different angles, all Machine Learning algorithms applied on the famous generic must-know case study “Iris Flowers”, as well as on “Pulse”, a real healthcare data set related to hospitalized patients. This exclusive approach, will allow participants to compare all solutions and adapt the exact one that fits their need in real life.

Learning Outcome

By the end of this workshop, participants will be able to compare systematically all “predictive” and “exploratory” Machine Learning algorithms, and more importantly their efficient application in their daily businesses. It will also clarify the “reality” of Artificial Intelligence and the science behind it, such as Neural Networks, image processing, Natural Language Processing and how all of them relate in the design of an AI project.

Participants will also have a comparative processing of some case studies under a multitude of technologies, of which some are “open source” (R and Python) and “proprietary” ones (SAS - SPSS – STATISTICA – Excel), allowing them to have a professional opinion in comparing different technologies.

A complete exhaustive material will be delivered in the workshop, detailing all the necessary information for all the topics.

Key Topics

Roadmap to Machine Learning in brief
- Data Vizualisation
- Descriptive statistics
- Data Anaysis

Unspervised Machine Learning
- Principal Component Analysis
- Clustering techniques

Natural Language Processing
Word2Vec
AI applications in the healthcare industry
Covid-19 and AI applications

Supervised Machine Learning
- Multiple Regressions
- Discriminant Analysis
- Naive Bayes
- Decision Trees
- KNN
- Support Vector Machines

Aritificial Neural Networks
- The FFNN Network
- Deep Learning models
- Convolutional Neural Networks
- Recurrent Neural Networks
- LSTM

Who should attend?

Researchers
Statisticians
Data Analysts
Data Scientists
Machine Learning modelers
Deep Learning software developers
AI practitioners
Computer science and IT managers

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Schedule:

Day 1

1stSession (1h30)

Part 1: The Roadmap to Artificial Intelligence

Statistics
Data Analysis
Machine Learning
Neural Networks
Deep Learning
NLP

Part 2: Prior knowledge before Machine Learning (Statistics and Data Analysis)

Data Vizualisation: Histograms, pie charts, heat maps, combo charts
Descriptive statistics: Mean, medan, mode, variance, standard deviaion
Data Analysis: t-test, F Anova, Chi Square and simple regressions

Part 3: Multiple Linear and Logistic Regressions(“Supervised“ ML)

Geometrical models

- Coefficients and model validation

- Coefficient of determination vs. correlation

Probabilistic prediction

- Coefficientsand model validation

- Odds ratio

2ndSession (1h30)

Part 1: Discriminant Analysis(“Supervised“ ML)

Discriminant vs. Classification functions
Mahalanobis Squared distances
Probabilistic models

Part 2: Decision Trees(“Supervised“ ML)

CART tree: growing, pruning and validating methods
CHAID tree
Random Forest

Part 3: K Nearest Neighbors(“Supervised“ ML)

Types of neighboring distances
Rules of classification
Voting method

Day 2

1stSession (1h30)

Part 1: Naive Bayes(“Supervised“ ML)

Bayes Theorem
Naïve assumption
CClassification rule

Part 2: Support Vector Machines(“Supervised“ML)

Predictive model
Estimation model
Kernel trick

Part 3: Artificial Neural Networks(“Supervised“ ML)

Neurons, Hidden layers
Perceptron , Synapsis, Weights, Scores
Activation functions: Sigmoid, TanH, ReLU
Classification with the SoftMax rule
Google Neural Network live simulator

2ndSession (1h30)

Part 1: Convolutional Neural Networks(Deep Learning)

AI revolution with image recognition and captioning
Convolution layers: Strides / Features / Filters
ConvNet / Pads
Pooling and Fully connected layers

Part 2: Recurrent Neural Networks(Deep Learning)

The four types of RNNs
Flow of data
Short-term memory
Vanishing and exploding gradient

Part 3: Long- and Short- Term Memory (Deep Learning)

Cell state
Forget phase
Update phase
Convey phase

Day 3

1stSession (1h30)

Part 1: All is possible with Big Data and IoT(Big Data and IoT)

What is IoT?
M2M and embedded systems
What is big data?
BD ecosystem vs. traditional IT
The 5 Vs of big data

2ndSession (1h30)

Part 1: Principal Component Analysis(“Unsupervised“ ML)

Observations vs. Variables analysis
Quality of data reduction:Eigenvalues
Low vs. High number of observations

Part 2: Clustering Analysis(“Unsupervised“ ML)

Clustering process
Parameters: distances and agglomerative rules
Clustering on top of a PCA?
Non Hierarchical clustering
K Mean clustering

Day 4

1stSession (1h30)

Part 1: Natural Language Processing (NLP)

Fields of application: Sentiment Analysis, Text categorization …
Tokenization, Stemming, Lemmatization, POS tags
Named Entity Recognition

Part 2: Word Embeddings(NLP)

Hot and Word Vectors
Word2Vec: CBOW vs. SKIP GRAM

2ndSession (1h30)

Part 1: AI solutions in the Pharma / Healthcare industry

Managing medical records
Clinical diagnosis with image recognition
Digital nurses / Digital consultation
Health monitoring / Smart hospitals

Part 2: Case study of “Covid-19 ... last of the pandemics ?“

Data design
Collecting data with IoT
Channeling data with big data technologies
Analyzing data with machine learning
Streaming outputs with automated applications

Click here to Grab your tickets now

About the Trainer

Walid Semaan is the founder and president of Matrix TRC "Data Science and AI Academy (certified by SAS international and partners with Abu Dhabi Business School). He graduated in engineering from Ecole Supérieure d’Ingénieurs à Beyrouth and holds a degree in finance and marketing from the Ecole Supérieure de Commerce de Paris (ESCP) and an MBA from the University Paris-Dauphine-Sorbonne in Paris.

He is the creator and architect of the automated analytical Artificial Intelligence behind “Triple One Analytics,“ winner of the Best Innovative ICT Project at the 2011 Arab Golden Chip Award. Walid is the trainer of all the workshops he continuously updates in parallel with the training and consulting delivered worldwide, related to research methodologies, data visualization, data analysis, machine learning, deep learning, and NLPs, as well as complete forecasting, statistical quality control, and epidemiology programs.

Walid joined lately VOSTAD Edx as an expert trainer in Data Science & AI and is an expert certified trainer in the Middle East for SAS, PWC Academy, MEIRC / Training PLUS, and Formatech.

In parallel, he holds thousands of hours teaching master programs at Saint Joseph University, assisting Ph.D. students in their advanced analytics and machine learning programs, and training local and international companies, to name few from hundreds: Central Bank of Lebanon (Lebanon), PWC (Dubai), SCAD (Abu Dhabi), OXY Petroleum (Oman), Government Statistics (Ajman - UAE), IPSOS (Lebanon), Dallah Hospital (KSA), Smart Dubai (Dubai), DarkMatter (Abu Dhabi), Indevco Industries (Lebanon and Egypt), Sanofi (Amsterdam).