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Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN MIAMI, USA

Live Virtual

Instructor Led Live Online

2,890
1,864

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

1,730
1,116

  • Self Learning + Live Mentoring
  • IABAC® & DMC Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING ARTIFICIAL INTELLIGENCE TRAINING SCHEDULES IN MIAMI

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The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR ARTIFICIAL INTELLIGENCE ONLINE COURSE

Why DataMites Infographic

SYLLABUS OF AI COURSE IN MIAMI

MODULE 1 : DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2 : DATA SCIENCE ESSENTIALS 

  • Introduction to Data Science
  • Evolution of Data Science
  • Data Science Terminologies
  • Data Science vs AI/Machine Learning
  • Data Science vs Analytics

MODULE3 : DATA SCIENCE DEMO 

  • Business Requirement: Use Case
  • Data Preparation
  • Machine learning Model building
  • Prediction with ML model
  • Delivering Business Value

MODULE 4 : ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5 : DATA SCIENCE AND RELATED FIELDS 

  • Introduction to AI
  • Introduction to Computer Vision
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning
  • Introduction to GAN
  • Introduction to  Generative Passive Models

MODULE 6 : DATA SCIENCE ROLES & WORKFLOW

  • Data Science Project workflow
  • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
  • Data Science Project stages

MODULE 7 : MACHINE LEARNING INTRODUCTION

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 8 : DATA SCIENCE INDUSTRY APPLICATIONS 

  • Data Science in Finance and Banking
  • Data Science in Retail
  • Data Science in Health Care
  • Data Science in Logistics and Supply Chain
  • Data Science in Technology Industry
  • Data Science in Manufacturing
  • Data Science in Agriculture

MODULE 1 : PYTHON BASICS 

  • Introduction of python
  • Installation of Python and IDE
  • Python objects
  • Python basic data types
  • Number & Booleans, strings
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Operator’s precedence and associativity

MODULE 2 : PYTHON CONTROL STATEMENTS 

  • IF Conditional statement
  • IF-ELSE
  • NESTED IF
  • Python Loops basics
  • WHILE Statement
  • FOR statements
  • BREAK and CONTINUE statements

MODULE 3 : PYTHON DATA STRUCTURES 

  • Basic data structure in python
  • String object basics and inbuilt methods
  • List: Object, methods, comprehensions
  • Tuple: Object, methods, comprehensions
  • Sets: Object, methods, comprehensions
  • Dictionary: Object, methods, comprehensions

MODULE 4 : PYTHON FUNCTIONS 

  • Functions basics
  • Function Parameter passing
  • Iterators
  • Generator functions
  • Lambda functions
  • Map, reduce, filter functions

MODULE 5 : PYTHON NUMPY PACKAGE 

  • NumPy Introduction
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations

MODULE 6 : PYTHON PANDAS PACKAGE 

  • Pandas functions
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations

MODULE 4: ML ALGO: LINEAR REGRESSSION 

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works : K Means theory
  • Modeling in Python

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSION 

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works: K Means theory
  • Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

  • Introduction to Naive Bayes
  • How it works: Bayes' Theorem
  • Naive Bayes For Text Classification
  • Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

  • Introduction to SVM
  • How It Works: SVM Concept, Kernel Trick
  • Modeling and Evaluation of SVM in Python

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

  • Introduction to ANN
  • How It Works: Back prop, Gradient Descent
  • Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set : Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

  • What is Time Series?
  • Trend, Seasonality, cyclical and random
  • Autoregressive Model (AR)
  • Moving Average Model (MA)
  • Stationarity of Time Series
  • ARIMA Model
  • Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

  • Regex Introduction
  • Regex codes
  • Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK 

  • Introduction to Flask
  • URL and App routing
  • Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

  • MS Excel core Functions • Pivot Table
  • Advanced Functions (VLOOKUP, INDIRECT..)
  • Linear Regression with EXCEL
  • Goal Seek Analysis
  • Data Table
  • Solving Data Equation with EXCEL
  • Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

  • Introduction to AZURE ML studio
  • Data Pipeline and ML modeling with Azure
  • MODULE 1: DATABASE INTRODUCTION 

    • DATABASE Overview
    • Key concepts of database management
    • CRUD Operations
    • Relational Database Management System
    • RDBMS vs No-SQL (Document DB)

    MODULE 2: SQL BASICS 

    • Introduction to Databases
    • Introduction to SQL
    • SQL Commands
    • MY SQL  workbench installation
    • Comments • import and export dataset

    MODULE 3: DATA TYPES AND CONSTRAINTS 

    • Numeric, Character, date time data type
    • Primary key, Foreign key, Not null
    • Unique, Check, default, Auto increment

    MODULE 4: DATABASES AND TABLES (MySQL) 

    • Create database
    • Delete database
    • Show and use databases
    • Create table, Rename table
    • Delete table, Delete  table records
    • Create new table from existing data types
    • Insert into, Update records
    • Alter table

    MODULE 5: SQL JOINS 

    • Inner join
    • Outer join
    • Left join
    • Right join
    • Cross join
    • Self join

    MODULE 6: SQL COMMANDS AND CLAUSES 

    • Select, Select distinct
    • Aliases, Where clause
    • Relational operators, Logical
    • Between, Order by, In
    • Like, Limit, null/not null, group by
    • Having, Sub queries

    MODULE 7: DOCUMENT DB/NO-SQL DB 

    • Introduction of Document DB
    • Document DB vs SQL DB
    • Popular Document DBs
    • MongoDB basics
    • Data format and Key methods
    • MongoDB data management

MODULE 1: GIT  INTRODUCTION 

  • Purpose of Version Control
  • Popular Version control tools
  • Git Distribution Version Control
  • Terminologies
  • Git Workflow
  • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

  • Git Repo Introduction
  • Create New Repo with Init command
  • Copying existing repo
  • Git user and remote node
  • Git Status and rebase
  • Review Repo History
  • GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

  • Code commits
  • Pull, Fetch and conflicts resolution
  • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

  • Organize code with branches
  • Checkout branch
  • Merge branches

MODULE 5: UNDOING CHANGES 

  • Editing Commits
  • Commit command Amend flag
  • Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

  • Creating GitHub Account
  • Local and Remote Repo
  • Collaborating with other developers
  • Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
  • Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs Hadoop Hive
  • Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML 

  • Introduction to MLlib Various ML algorithms supported by MLib
  • ML model with Spark ML
  • Linear regression
  • logistic regression
  • Random forest

MODULE 6: KAFKA and Spark 

  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

  • What Is Business Intelligence (BI)?
  • What Bi Is The Core Of Business Decisions?
  • BI Evolution
  • Business Intelligence Vs Business Analytics
  • Data Driven Decisions With Bi Tools
  • The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION 

  • The Tableau Interface
  • Tableau Workbook, Sheets And Dashboards
  • Filter Shelf, Rows And Columns
  • Dimensions And Measures
  • Distributing And Publishing

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

  • Connecting To Data File , Database Servers
  • Managing Fields
  • Managing Extracts
  • Saving And Publishing Data Sources
  • Data Prep With Text And Excel Files
  • Join Types With Union
  • Cross-Database Joins
  • Data Blending
  • Connecting To Pdfs

MODULE 4 : TABLEAU : BUSINESS INSIGHTS 

  • Getting Started With Visual Analytics
  • Drill Down And Hierarchies
  • Sorting & Grouping
  • Creating And Working Sets
  • Using The Filter Shelf
  • Interactive Filters
  • Parameters
  • The Formatting Pane
  • Trend Lines & Reference Lines
  • Forecasting
  • Clustering

MODULE 5 : DASHBOARDS, STORIES AND PAGES 

  • Dashboards And Stories Introduction
  • Building A Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Story Points
  • Animation With Pages

MODULE 6 : BI WITH POWER-BI 

  • Power BI basics
  • Basics Visualizations
  • Business Insights with Power BI

MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW 

  • Evolution Of Human Intelligence
  • What Is Artificial Intelligence?
  • History Of Artificial Intelligence
  • Why Artificial Intelligence Now?
  • Ai Terminologies
  • Areas Of Artificial Intelligence
  • Ai Vs Data Science Vs Machine Learning

MODULE 2: DEEP LEARNING INTRODUCTION 

  • Deep Neural Network
  • Machine Learning vs Deep Learning
  • Feature Learning in Deep Networks
  • Applications of Deep Learning Networks

MODULE 3: TENSORFLOW FOUNDATION 

  • TensorFlow Installation and setup
  • TensorFlow Structure and  Modules
  • Hands-On: ML modeling with TensorFlow

MODULE 4: COMPUTER VISION INTRODUCTION 

  • Image Basics
  • Convolution Neural Network (CNN)
  • Image Classification with CNN
  • Hands-On: Cat vs Dogs Classification with CNN Network

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) 

  • NLP Introduction
  • Bag of Words Models
  • Word Embedding
  • Language Modeling
  • Hands-On: BERT  Algorithm

MODULE 6: AI ETHICAL ISSUES AND CONCERNS 

  • Issues And Concerns Around Ai
  • Ai And Ethical Concerns
  • Ai And Bias
  • Ai: Ethics, Bias, And Trust

MODULE 1: NEURAL NETWORKS 

  • Structure of neural networks
  • Neural network - core concepts
  • Feed forward algorithm
  • Backpropagation
  • Building neural network from scratch using Numpy

MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS 

  • Introduction to neural networks with tf2.X
  • Simple deep learning model in Keras (tf2.X)
  • Building neural network model in TF2.0 for MNIST dataset

MODULE 3: DEEP COMPUTER VISION - CNN 

  • Convolutional neural networks (CNNs)
  • Introduction
  • CNNs with Keras
  • Transfer learning in CNN
  • Style transfer
  • Flowers dataset with tf2.X
  • Examining x-ray with CNN model

MODULE 4 : RECURRENT NEURAL NETWORK 

  • RNN introduction
  • Sequences with RNNs
  • Long short-term memory networks
  • LSTM RNNs and GRU
  • Examples of RNN applications

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) 

  • Natural language processing
  • Introduction
  • NLP with RNNs
  • Creating model
  • Transformers and BERT
  • State of art NLP and projects

MODULE 6: REINFORCEMENT LEARNING 

  • Markov decision process
  • Fundamental equations in RL
  • Model-based method
  • Dynamic programming model free methods

MODULE 7: DEEP REINFORCEMENT LEARNING 

  • Architectures of deep Q learning
  • Deep Q learning
  • Policy gradient methods

MODULE 8: GENERATIVE ADVERSARIAL NETWORK (GAN) 

  • Gan introduction
  • Core concepts of GAN
  • Building GAN model with TensorFlow 2.X
  • GAN applications

MODULE 9: DEPLOYING DL MODELS IN THE CLOUD (AWS) 

  • Amazon web services (AWS)
  • AWS SageMaker Overview
  • Sage Makers from Data pipeline to deployments
  • Deploying deep learning models WS Sage maker

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN MIAMI

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN MIAMI

Artificial Intelligence has grown to be a huge discipline and is also widely supporting organizations in addressing the complexities.  Like any other field,  Artificial Intelligence also made a place for itself in the technological world.  The reasons to believe in the role of AI, in helping to bring change, are many. The market for Artificial Intelligence jobs is in a stage of boom. According to the U.S. based advisory firm Gartner, AI is expected to create a business value of $3.9 trillion in 2022. According to the World Economic Forum, 133 million new jobs are expected to be created by 2022 in the field of AI.

Miami boasts a robust and diverse economy that fuels its status as a global business hub. With a GDP of over $300 billion, the city has experienced steady growth in recent years. The tourism sector remains a key driver, attracting more than 23 million visitors annually. Miami's international trade, particularly with Latin America and the Caribbean, is flourishing, with an estimated $160 billion in annual trade volume. The city's real estate market is thriving, and its booming tech and startup scene further contribute to its economic vibrancy.

Learning Artificial Intelligence (AI) is an exceptional investment in your professional journey. AI has revolutionized industries worldwide, and its demand continues to surge. By mastering AI, you unlock countless opportunities across various sectors, including healthcare, finance, technology, and more. The skills you acquire will make you a highly sought-after professional, as organizations increasingly rely on AI for data analysis, automation, and decision-making. By understanding AI's foundations, algorithms, and applications, you can shape the future and contribute to groundbreaking advancements. Embracing an Artificial Intelligence Course equips you with the knowledge and tools to stay ahead in an ever-evolving job market, where AI proficiency is a highly coveted skill. 

The Artificial Intelligence Engineer course offered by DataMites in Miami is an industry-oriented certification which is designed in tune with the best knowledge standards and keeping in mind, the long term objective of helping the aspirants, in gaining professional excellence in their organizational endeavors. The Artificial Intelligence Engineer course covers - Artificial Intelligence Foundation, Machine Learning, Neural Networks, Computer Vision, Natural Language Processing. 

DataMites, in Miami, offers dual certification programs, in collaboration with IABAC and IBM, that are recognized on a global platform. The courses are accredited to IABAC- Globally recognized body for Business Analytics and Data Science Certifications. A duration of 11 months with 780 Hrs of training.Faculties with rich industry experience, and intense subject matter expertise.Guidance for resume building, interview preparations, expanding networks for job possibilities.Internship opportunities, with 10 capstone projects and 1 live project.

DataMites also provides training for Machine Learning, Certified Data Analyst, Deep Learning, Python, IoT, Python for Data Science, and the entire Data Science courses in Miami.

DESCRIPTION OF ARTIFICIAL INTELLIGENCE COURSE IN MIAMI

Artificial Intelligence is a branch of Computer Science which talks about incorporating the reasoning and decision making capabilities demonstrated by humans, into a machine, which makes it possible for the machine to exercise the critical tasks which require human intervention.

The Artificial Intelligence Engineer course offered by DataMites consists of a bundle of different courses- Artificial Intelligence Foundation, Machine Learning, Tensorflow 2.X Platform, Core Learning Algorithms, Neural Networks, Implementing Deep Neural Networks, Reinforcement Learning, Natural Language Processing, etc. 

The Artificial Intelligence Engineer is the most comprehensive course with the following features:- 

  • Dual Certification- IABAC and IBM (Best in class industry certification)
  • 6 months of live online training.
  • Training by industry experts.
  • Internship Opportunities(10 Capstone Projects and 1 Client Project)

Machine Learning is a branch of Artificial Intelligence, which concerns the ability of machines to learn from experience and subsequently improve themselves, without being influenced by another person.

Deep Learning is a part of Artificial Intelligence and Machine Learning. To be precise, when the data is huge in numbers, Machine Learning doesn’t hold good, as they are incapable of going deep into the data sets. Deep Learning helps to address this problem.  The structure of Deep Learning comprises Artificial Neural Networks which resemble the neuron structure in the human brain. These networks have different layers and are capable enough to pierce inside the large data set to retrieve the relevant information. 

The prerequisites to pursue an  AI Engineer course are:

Educational Qualifications

  •  Graduation/PG in Computer Science, IT, Statistics
  •  Certification in Data Science, Machine Learning, Deep Learning, etc.

Some of the technical skills that would prove advantageous in learning an Artificial Intelligence course are:-

  • Knowledge of Mathematics and Statistics.
  • Knowledge of Algorithms.
  • Knowledge of programming languages- C, C++, Java
  • Knowledge of Neural Networks
  • Knowledge of Natural Language Processing- NLP Libraries

 

Some of the business skills that would prove advantageous in learning an Artificial Intelligence course are:-

  • Analytical Skills
  • Problem Solving 
  • Communication Skills
  • Business Acumen

Python is the most preferred among programming languages in the field of Data Science and Artificial Intelligence. As far as Data Scientist is concerned Python is the most effective programming language, with a lot of libraries available. Python can be deployed at every phase of data science functions. It is beneficial in capturing data and importing it into SQL. Python can also be used to create data sets. 

The Artificial Intelligence course offered by DataMites comprises a topic on Python Programming language. Having a basic understanding of Python is an added advantage for the Artificial Intelligence course.

Machine Learning and Artificial Intelligence are two inter-related topics. The Artificial intelligence course provided by DataMites comprises Machine Learning as a part of its syllabus. However, a basic knowledge of Machine Learning would be an advantage while joining the course.

Yes. The Artificial Intelligence course provided by DataMites covers a topic on Python. It includes concepts such as Building ML Classification Models with Python, Building ML Regression Models with Python, CIFAR-10 classification with Python, Transfer Learning In Python, RNNS In Python 

DataMites offers an AI Engineer course at an affordable cost of $1620.

P.G degree is not a mandatory requirement to pursue an Artificial Intelligence certification. However, a sound knowledge of Technology, Engineering, and Management domains will be an added advantage.

Artificial Intelligence is present everywhere nowadays and is used across functions like Finance, Healthcare, Education, Manufacturing, Retail, Customer Service, etc. Therefore learning Artificial Intelligence will help to increase the chances of your employability in various sectors. AI is also an indispensable factor, for the reason that most of the data today are stored digitally. The potential of AI to be incorporated into data helps in making the right decisions.

The Artificial Intelligence course in Miami offered by DataMites helps to give you a clear picture of the role of AI in the decision making and the problem-solving process.

This Artificial Intelligence course enables you to:-

  • Understand AI and its relevance in bringing in change in the current industrial scenario.
  • Learn the terminologies that are used in the AI domain.
  • Gain practical knowledge of employing AI and related disciplines in solving complex real-world problems.
  • Make decision making easy.

Miami is known for lots of business opportunities and large corporate houses adorning the city. This, in turn, contributes to new employment opportunities being created. 

Learning the Artificial Intelligence course in Miami helps you to leverage the available opportunities and also prepares you for the challenges. Artificial Intelligence is a discipline that is influencing the present in a big way and is expected to grow in the future. Therefore by learning AI you are at the advantage of remaining well equipped in advance to cope with the changing times.

DataMites in Miami offers the most comprehensive Artificial Intelligence course that is aligned with the state of art industry best practices in the Artificial Intelligence domain. 

Miami has a lot of business opportunities with large corporates gracing the city. The career opportunities in Artificial Intelligence are booming and Miami is no exception.

DataMites in Miami provides the most comprehensive Artificial Intelligence Engineer course with the following features.

  • Dual Certification- IABAC and IBM(Best in class industry certification)
  • Experienced Trainers
  • Industry aligned courses
  • Internship Opportunities
  • Job assistance

DataMites caters to graduates and professionals equally. Therefore, DataMites is the best choice for anyone who wishes to become an Artificial Intelligence Engineer in Miami.

Miami, in the U.S,A is known for lots of business opportunities. It consists of many large companies, business houses, with large amounts of transactions happening every day, as a result of which there is an equally large amount of data generated daily. Also, the U.K. is known for many recognized universities. Learning Artificial Intelligence in the U.K will be a great opportunity for students as well as professionals.

DataMites in Miami provides the most comprehensive Artificial Intelligence course that is designed as per the current industry requirements. Also, the Artificial Intelligence course provided by DataMites in Miami is dually certified in collaboration with IABAC and IBM.

On completing the Artificial Intelligence course DataMites in Miami you will be eligible for the following job roles:-

  • Artificial Intelligence Engineer
  • Data Scientist
  • Machine Learning Expert
  • Analytics Manager

The market for Artificial Intelligence in Miami is booming and is expected to grow in the future. As AI requires the mastering of various disciplines and there are only a few who are good at all of them, the one who can master all the disciplines is at a greater advantage. Career Opportunities in AI are plenty but there is a shortage of skilled AI professionals, therefore there is also a rising demand for the same. Some of the top industries in Miami for AI are- Banking and Finance,  Information and Communication, Administration, and Support Services.

According to Indeed.com the average salary of an Artificial Intelligence Expert in Miami is $113,857 per year.

The U.S.A has a good number of small, medium, and large corporations. The opportunity in Artificial Intelligence in the U.S.A is also in plenty. As AI has shown us a way to tackle real-world complexities, the need to incorporate AI into various functions is equally important. All the present-day organisations are well aware of this and have acknowledged this to a great extent.  In simple words, most of the companies nowadays have found a better way of tackling their day- to day problems with the help of AI. 

Every company in the U.S.A(Be it Small, Medium, and Large enterprises) requires AI professionals as all of them work on their data and requires some or the other AI expertise to be deployed into the tasks.

Artificial Intelligence, Machine Learning and Data Science contribute to one another in one or the other way.  Python and R are the two programming languages that are used in the data science process. Some of the reasons, for python being the most preferred programming language in comparison to R:-

  • Easy to learn: Python is easier to understand and master, in comparison to R 
  • Flexible: The flexibility offered by Python offers is better when compared to the R programming language.
  • Availability of libraries:- Python has a wide range of libraries available, such as pandas, scikit-learn, etc. This makes it easier in handling machine learning projects.
  • Data visualization: By using matplotlib in Python, you can do the plotting of complex data representations into 2D plots. Data visualization is a significant process in the job of a data scientist. Python can be used for Data Visualisation. 

However as far as Artificial Intelligence is concerned, learning both Python and R will be advantageous.

The instructors at DataMites institute are industry experts who have a good number of years of experience in the field of Artificial Intelligence.

Enrolling in online training online is very simple. The payment can be done using your debit/credit card that includes Visa Card, MasterCard; American Express, or PayPal. You will receive the receipt after the payment is successful. You can get in touch with our educational counsellor for more information.

DataMites conducts classes for Artificial Intelligence courses both during Weekdays and Weekends. You can opt between the two according to your convenience.

DataMites conducts both morning and evening classes for Artificial Intelligence courses in Miami. You can opt between the two as per your convenience.

Yes. DataMites provides an online lab facility called Pro Lab. You can log in with a username to use this facility.

Yes, DataMites has partnered with many AI companies and provides live Artificial Intelligence projects to work on which helps the candidate to get exposure to the real-world working environment. DataMites provides 10 Capstone projects and 1 client project as part of the Artificial Intelligence course.

The DataMites Placement Assistance Team(PAT)  helps the candidates to have an easy start in his/her career. The team offers services like Resume Building, Interview Preparation. The team will assist you in the following areas;-

  • Project Mentoring- 100 hrs Live mentoring in industry projects.
  • Interview Preparations- Mock Interview sessions.
  • Resume Support- Personal guidance in resume creation by professionals.
  • Doubt clearing sessions- Live doubt clearing sessions on 
  • Job updates- Interview connects.
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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN MIAMI

The training provided by DataMites for Artificial Intelligence in Miami is primarily online. However, classroom training can be made available if there is adequate demand.

 

DataMites is a global institute that offers comprehensive courses in Artificial Intelligence. The syllabus is designed in tune with the current industry trends and helps to cater to the needs of fresh AI aspirants and experienced professionals. The Artificial Intelligence course offered by DataMites is unique in the following ways

DataMites offers an Artificial Intelligence certification in Miami at an affordable fee/cost of $1620.

DataMites in Miami offers dual certifications in collaboration with IABAC and IBM. IABAC is a global body, which offers certifications in Business Analytics and Data Science. IABAC is founded on the principles of the EDISON Data Science Framework (EDSF). IBM provides the best in class industry certifications. DataMites provides a range of certifications in Data Science, Machine Learning, Artificial Intelligence. All the data science certifications offered by DataMites are structured based on the industry trends.

DataMites provides online training sessions for the Artificial Intelligence course in Miami. However, classroom mode of training can be made available if there is adequate demand for the same.

Yes. DataMites offers internship opportunities for the Artificial Intelligence course which helps you to get exposure,  understand and implement the concepts learned in the course to build AI models for solving real-world problems. DataMites provides 10 Capstone projects and 1 client project for the Artificial Intelligence course. 

Yes. You will learn Deep Learning as a part of the AI Engineer course. It includes - Layers, Loss Function, Optimization, Model Training, and Evaluation, etc. 

Yes. You will learn Computer Vision as a part of the Artificial Intelligence course. It includes - Convolutional Neural Networks, CNN with KERAS, Transfer Learning, etc.

Yes. You will learn Neural Networks as a part of the Artificial Intelligence course. It includes - Core Concepts of Neural Networks, Structure of Neural Networks, Back Propagation, etc.  

The Artificial Intelligence course offered by DataMites in Miami covers the following topics:-

  • Artificial Intelligence Foundation.
  • Machine Learning 
  • Tensorflow
  • Core Learning Algorithms 
  • Neural Networks
  • Natural Language Processing(NLP)
  • Deep Computer Vision- Convolutional Neural Networks
  • Reinforcement Learning.

The duration of the Artificial Intelligence course provided by DataMites in Miami is 11 months with 780 hrs of live online training conducted by industry experts. 

Artificial Intelligence is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Miami offers quality training sessions in Artificial Intelligence, Machine Learning, etc. The Artificial Intelligence courses provided by DataMites in Miami are exclusively designed in tune with the current industry requirements. Also with many projects to work on, under the mentoring of industry experts. 

DataMites offers an Artificial Intelligence course in Miami at an affordable price of $1620.

The registrations canceled within 48 hrs of enrollment will be refunded in full. The processing time of the refund is within 30 days, from the date of the receipt of the cancellation request

You have access to the online study materials from 6 months up to 1 year.

DataMites accepts all the online payments(Debit/Credit)for the AI course in Miami through Razor pay. If you opt to pay through your credit card there will be an EMI option. DataMites collect token advance during the time of registration and the remaining payment should be settled in full before the completion of the course. 

All the online sessions are recorded. If you happen to miss a session you can access the online recording.

Yes. The Artificial Intelligence certification exam fee is included in the total course fee. Therefore once you are registered for a course, you are also eligible to attend the exam.

Yes. You will learn Natural Language Processing(NLP) as a part of the Artificial Intelligence course. It includes - The Basics of Natural Language Processing, Integer Coding, Word Embedding, and Bag Of Words. 

Yes. One of the courses out of the bundle of AI course talks about Reinforcement Learning. It includes- Markov Decision Process, Fundamental Equations in Reinforcement Learning. 

Yes. One of the courses out of the bundle of AI course talks about Tensorflow. It includes-Basics of Tensorflow, Installation and Basic Operation in Tensorflow, Tensorflow 2.0 Eager Mode.

Yes. One of the courses out of the bundle of AI course talks about Machine Learning. It includes-Basics of Machine Learning, Mathematics for Machine Learning. 

Yes. One of the courses out of the bundle of AI course talks about Python. It includes

Yes, the  Artificial Intelligence Engineer course provided by DataMites comprises a topic on Machine Learning in the syllabus. Therefore when you learn the AI course, you also get an opportunity to learn Machine Learning. The Machine Learning topics covered are:-

Machine Learning Overview, Mathematics for Machine Learning, Advanced Machine Learning Concepts, etc.

Yes. DataMites will provide you with a course completion certificate after you clear the AI certification examination.

The AI course offered by DataMites in Miami includes 10 capstone projects and 1 client project.

The mode of training offered by DataMites in Miami is primarily online. However, classroom training can be made available in Miami,  if there is adequate demand for the same.

DataMites is a global institute for Artificial Intelligence education. It has a history of training for more than 15000 candidates. The syllabus provided by DataMites in Miami is exclusively designed in tune with the current industry trends. The following makes DataMites unique from others:-

  • Dual Certification- IABAC and IBM(Best in class industry certification)
  • Experienced Trainers
  • Industry aligned courses
  • Internship Opportunities
  • Career Guidance
  • More than 15000 certified learners

DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The Flexi Pass is specific to one particular course. Therefore if you have a Flexi pass for a particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a Flexi pass is valid for a particular period.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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