Explore AI, ML and DL Differences, Advantages, Modern Uses. Techniques for Adoption, Tools, Algorithm's and More

Introduction to AI, Machine Learning & Deep Learning Essentials is an engaging, hands-on training program designed to provide students new to these areas with a baseline understanding of the core technologies, skills, business application and tools surrounding them. These fast growing, critical technologies are currently shaping the future of IT, development and analytics.

This program combines hands-on machine-based labs, live demonstrations and discussions that explore current trends, tools and skills, as well as advances in these areas. Working in a hands-on manner, attendees will gain a basic understanding of terms, skills and capabilities in this technology stack, providing them with a solid foundation for next-step learning as they pursue defined roles in these areas.

Is This The Right Course?

The general pre-requisite items below would be helpful for attendees to familiarize themselves with in order to gain the most from the discussions and hands-on labs work planned for each general related skills area listed below. Students without supporting experience in certain areas can plan to follow along with labs or utilize them as demonstrations.

Some of the related useful skills

Required - Enterprise IT / Business Knowledge: Attendees should have some familiarity with Enterprise IT as well as a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying data science, AI and machine learning.
Recommended - Advanced Math / Statistics: Advanced math and essentials statistics knowledge is useful in understanding and working with Algorithms
Recommended – Basic Language / Scripting Knowledge: Basic Python (or R) scripting is applicable to machine learning and deep learning. Basic Java is useful for working with some of the advanced tools such as Spark or TensorFlow.

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We can host this training at your preferred location.

Prerequisites

Some of the related useful skills

Required - Enterprise IT / Business Knowledge: Attendees should have some familiarity with Enterprise IT as well as a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying data science, AI and machine learning.
Recommended - Advanced Math / Statistics: Advanced math and essentials statistics knowledge is useful in understanding and working with Algorithms
Recommended – Basic Language / Scripting Knowledge: Basic Python (or R) scripting is applicable to machine learning and deep learning. Basic Java is useful for working with some of the advanced tools such as Spark or TensorFlow.

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What You Will Learn

Led by our expert AI / Machine Learning practitioner, students will learn about and explore:

The What and Why of AI, Machine Learning & Deep Learning – why is this important and exciting?
Getting the Basics: High-level skills, vocabulary and terminology
AI, Machine Learning and Deep Learning – what are the differences and uses?
Latest trends and research
Who’s Using It and to What Advantage?
How to adopt AI, ML and DL
Hands-on Machine Learning – algorithms, neural networks, natural language processing & more
Tools and Languages: Python, R, Spark, TensorFlow, Keras
Deep Learning Essentials

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Outline

Exploring Data Science – The Foundation of AI, Machine Learning & Deep Learning

What is Data Science?
New Ways of Thinking about and using Data
Challenges of processing
Technologies
Strategies
Where does data science fit in?
DS ecosystem – AI, Machine Learning, Deep Learning
Data and the Scientific Method
Data Science vs. Data Engineering
Sharing Results with Colleagues
Recording experiments
The Data Science Team members
Data Science Infrastructure
Current Tools, Trends & Technologies
Applying Data Science to Your Industry

Understanding AI

AI - How did we get here?
Recent advances in data, hardware
Cutting edge research and applications
Getting the basics: Core terms and vocabulary

Understanding Machine Learning

Who is leveraging this and why
Overview of ML – what’s the difference?
Related examples of ML algorithms and applications
Surrounding tools and technologies: Python and Spark

Machine Learning

Supervised vs. Unsupervised
Classification
Regression
Clustering
Dimensionality Regression
Ensemble Methods

Understanding Deep Learning

What is it, and how is this different than AI and ML?
Who’s using Deep Learning and Why
Deep Learning algorithms and applications
Surrounding tools and technologies: Python, TensorFlow, Keras

Expert Systems

Rules Systems
Feedback loops
RETE and beyond
Expert Systems in practice

Neural Networks

Neural Networks
Recurrent Neural Networks
Long-Short Term Memory Networks
Applying Neural Networks

Natural Language Processing

Language and Semantic Meaning
Bigrams, Trigrams, and n-Grams
Root stemming and branching
NLP in the world

Image, Video, and Audio Processing

Image processing and Identification
Facial Analysis
Audio Processing
Analyzing Streaming Video
Real-world AV processing

Sentiment Analysis

Sentiment: The beginnings of emotional understanding
Sentiment indicators
Sentiment Sampling
Algorithmic Trading on Sentiment
Predicting Elections

Current Tools of the Trade - AI, ML & DL - Software Ecosystem

Python, NumPy, Pandas, SciKit
Hadoop and Spark
NoSQL Databases
TensorFlow, Keras, and NLTK
Drools
Libraries
Cloud offerings

Making it Happen: How to Adopt AI & ML in Enterprises

Technology stack
Assembling an effective team
Process – how does this all come together
Best Practices – what do your people need to succeed

Resources – where to find more information

Time Permitting: Capstone Project

Hands-on guided workshop utilizing skills learned throughout the course

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Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

AI, Machine Learning & Deep Learning Essentials Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.

AI, Machine Learning & Deep Learning Essentials Training in Norway

Is This The Right Course?

Prerequisites

What You Will Learn

Outline

Avaible Training Dates