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Course Code: DIGTR 119
799 Course Visits
Introduction to Machine Learning and Artificial Intelligence
Course Sector:
Digital Transformation and Innovation
Course Dates and Locations
Choose a date and location to book your seat
No.
Date
Days
Location
Fees
Enrollment
01
5 Days
Online, Virtual $2,150
02
5 Days
Abu Dhabi, UAE $4,250
03
5 Days
Vienna, Austria $4,950
Introduction
Training course introducion / brief
Artificial Intelligence (AI) and Machine Learning (ML) are currently changing the game for the industries. Many businesses take up artificial intelligence (AI) technology to try to reduce operational costs, increase efficiency, grow revenue and improve customer experience. For greatest benefits, businesses should look at putting the full range of smart technologies - including machine learning, natural language processing and more - into their processes and products. However, even businesses that are new to AI can reap major rewards.
This training course is designed to provide participants with the fundamental concepts of AI and machine learning and allow them to have an in-depth understanding of its benefits and uses in the organisation.
Course Objectives
At the end of the training course, participants will be able to
- The basic concept and ideas of machine learning and artificial intelligence.
- Python and Jupyter
- Statistics and Probability Refresher and Python Practice
- Matplotlib and Advanced Probability Concepts
- Algorithm Overview
- Predictive Models
- Applied Machine Learning
- Recommender Systems
- Dealing with Data in the Real World
- Machine Learning on Big Data (with Apache Spark)
- Testing and Experimental Design
- GUIs and REST: Build a UI and REST API for your Models
Course Audience
Who is this course for, and can benefit the most
- Business and technology leaders
- Business Unit Managers
- Business Development Consultants
- General Managers / Regional Managers
- Senior and mid-level leaders
- individual leaders of all levels in the organization
- Art Director
- Marketing Consultants
- Marketing Development Manager
Course Outline
The course aims and learning outcomes
The Business of AI Adoption
Artificial Intelligence and its application Areas AI and Machine learning Pattern recognition Supervised and unsupervised learning, Structured and unstructured data Discussion on Industry, industrial analytics
- Defining artificial intelligence
- The uncertainties of new technology
- AI in the field
- The problem of trust
- Work is evolving
- Driverless transportation
- Trust and the machine
- The human-smart machine trust gap
- Trusting a smart machine
- Trusting the smart machine developer
Machine Learning
- Basic ideas of machine learning
- Bias-variance complexity trade-off
- Model types
- Deep neural network
- Recurrent neural network or long short-term memory network
- Support vector machines
- Random forest or decision trees
- Self-organizing maps (SOM)
- Bayesian network and ontology
- Training and assessing a model
- How good is my model?
- Role of domain knowledge
- Optimization using a model
- Installing a Python Data Science Environment
- Using and understanding iPython (Jupyter) Notebooks
- Python basics: Part 1
- Understanding Python code
- Importing modules
- Python basics: Part 2
- Running Python scripts
- Types of data
- Mean, median, and mode
- Using mean, median, and mode in Python
- Standard deviation and variance
- Probability density function and probability mass function
- Types of data distributions
- Percentiles and moments
Matplotlib and Advanced Probability Concepts
- A crash course in Matplotlib
- Covariance and correlation
- Conditional probability
- Bayes' theorem
- Data Prep
- Linear Algorithms
- Non-Linear Algorithms
- Ensembles
- Linear regression
- Polynomial regression
- Multivariate regression and predicting car prices
- Multi-level models
- Machine learning and train/test
- Using train/test to prevent overfitting of a polynomial regression
- Bayesian methods: Concepts
- Implementing a spam classifier with Naïve Bayes
- K-Means clustering
Recommender Systems
- What are recommender systems?
- Item-based collaborative filtering
- How item-based collaborative filtering works?
- Finding movie similarities
- Improving the results of movie similarities
- Making movie recommendations to people
- Improving the recommendation results
- K-nearest neighbors - concepts
- Using KNN to predict a rating for a movie
- Dimensionality reduction and principal component analysis
- A PCA example with the Iris dataset
- Data warehousing overview
- Reinforcement learning
- Bias/variance trade-off
- K-fold cross-validation to avoid overfitting
- Data cleaning and normalization
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers
Apache Spark: Machine Learning on Big Data
Build a UI for your Models Build a REST API for your Models
- Installing Spark
- Spark introduction
- Spark and Resilient Distributed Datasets (RDD)
- Introducing MLlib
- Decision Trees in Spark with MLlib
- K-Means Clustering in Spark
- TF-IDF
- Searching wikipedia with Spark MLlib
- Using the Spark 2.0 DataFrame API for MLlib
- A/B testing concepts
- T-test and p-value
- Measuring t-statistics and p-values using Python
- Determining how long to run an experiment for
- A/B test gotchas
Providers and Associations
Providing the best training services and benefits to our valued clients
Boost certificate of completion
BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.
ENDORSED EDUCATION PROVIDER
Flexible deadlines
Customized dates accordance to your schedule
Shareable Certificate
Earn certificate upon completion
COURSE METHODOLOGY
Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).
At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.
26
Feb
- 05 -
Days
Days
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Online, Virtual
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