Artificial Intelligence Engineer Training in Pittsburgh , US
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Pittsburgh at GoLogica Training in Artificial Intelligence (AI) Engineer offers an extensive journey from elementary AI concepts to high-end machine learning and deep learning techniques. The course has core topics such as AI fundamentals, model building, neural networks, and language processing, thus enabling the participants to attack some of the most important real-world challenges as AI engineers.
GoLogica Academic's Master Program features a structured curriculum, paving the way to Global scope.
Industry Experience
GoLogica having a 15+ years of experience on career transforming programs with industrial oriented Skills.
Latest AI Trends
GoLogica Advanced Programs delivers cutting-edge AI Training, offering insights into the latest trends.
Hands-on Experience
GoLogica emphasizes practical learning with exercises, projects to equip you with real world application.
Learners Achievement
Maximum Salary Hike
150%
Average Salary Hike
75%
Haring Partners
2000+
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Artificial Intelligence Engineer Program Details
Artificial Intelligence Engineering Training at GoLogica offered at Pittsburgh has been organized systematically for enthusiastic professionals as well as experienced IT experts to gain all the skills and knowledge that can bring an efficient as well as impactful career in AI and ML. The program provides unique foundational knowledge along with practical learning and advanced AI skills necessary in today's competitive job market. Topics of study include introductory concepts in AI up to complex machine learning algorithms and data processing techniques, which can be used to develop advanced realistic applications with AI models.
Artificial Intelligence and Machine Learning - Foundational Concepts of AI and ML with a special focus on the principles of Data Science and critical statistical techniques. This includes intense practice in probability, data exploration, and cleaning data skills of any AI engineer. From this foundational starting point, students will dive into machine learning, including supervised and unsupervised learning, clustering, classification, regression, and recommendation systems to equip them to face various data-driven challenges in all industries.
Then it follows with advanced learning, which introduces the student to more detailed knowledge about neural networks and their applications in some areas like image recognition, natural language processing, and time series analysis. Based on leading AI frameworks that include TensorFlow, Keras, and PyTorch, the participants carry out a series of hands-on labs that include designing, training, and fine-tuning neural networks. Such exercises are more like true challenges AI faces in the real world. Such examples include designing algorithms for facial recognition, sentiment analysis, or predictive modeling. Students also learn more about such architectures used in neural networks, which include CNNs and RNNs, to develop AI solutions that are powerful yet scalable.
In addition to the technical skills, it provides exposure to best practices in AI ethics and responsible AI development. GoLogica in Pittsburgh offers and educates learners on bias mitigation using AI, ethical data usage, and adherence to the standards of the industries. These principles added to AI help the students to build solutions that meet business needs but under guideline prescriptions of high trustworthiness and long-term availability.
New AI tools and techniques, among which the following will find a part of the program: automation, orchestration, and NLP, will be discussed to completely cover how AI can be applied to deliver business process optimization and innovation in real-life scenarios. For example, topics such as generative models and reinforcement learning would surely provide full coverage of how to apply AI in delivering business process optimization and innovation in real-life scenarios. It will be working with all the latest tools: computer vision with OpenCV, NLP with Spacy, and a version control system like Git so that students can efficiently develop, test, and deploy an AI project.
The Training of GoLogica Pittsburgh AI Engineer course has interactive learning with real-time projects that closely mimic industry needs. By the end of the course, students would tend to present a solid portfolio of AI projects with a strong hold on cross-domain applications making them stand-out candidates for AI Engineer, Data Scientist, and Machine Learning Engineer profiles. Using GoLogica in Pittsburgh, students can start their careers in AI with practical skills and a great professional edge for themselves.
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Artificial Intelligence Engineer Syllabus
Deep Learning with TensorFlow
Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Some representations are loosely based on interpretation of information processing and communication.
WEEK 14-16 50 Hours LIVE CLASS
HelloWorld with TensorFlow
Linear Regression
Nonlinear Regression
Logistic Regression
Activation Functions
CNN History
Understanding CNNs
CNN Application
Intro to RNN Model
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model
Applications of Unsupervised Learning
Restricted Boltzmann Machine
Collaborative Filtering with RBM
Introduction to Autoencoders and Applications
Autoencoders
Deep Belief Network
Artificial Intelligence (AI)
GoLogica is offering an instructor led extensive course on Artificial Intelligence(AI) course. Artificial Intelligence is also called as Machine intelligence. This domain is constantly evolving and many applications are slowly moving towards this platform .
WEEK 5-6 40 Hours LIVE CLASS
Artificial Intelligence Training.html
Introduction to Artificial Intelligence
Applications, Industries
and growth
Techniques used for AI
AI for everything
Different methods used for AI
Tradition Methods & New Methods
AI Agents
Introduction to Anaconda
Installation of Anaconda Python Distribution : For Windows
Mac OS
and Linux
Jupyter Notebook Installation
Jupyter Notebook Introduction
Variable Assignment
Basic Data Types: Integer
Float
String
None and Boolean; Typecasting
Creating
Accessing
and slicing tuples
Creating
accessing
and slicing lists
Creating
viewing
accessing
and modifying dicts
Creating and using operations on sets
Basic Operators: 'in', '+', '*', Functions
Control Flow
NumPy Overview
Properties
Purpose
and Types of ndarray
Class and Attributes of ndarray Object
Basic Operations: Concept and Examples
Accessing Array Elements: Indexing
Slicing
Iteration
Indexing with Boolean Arrays
Copy and Views
Universal Functions (ufunc)
Shape Manipulation
Broadcasting
Linear Algebra
Introduction to Pandas
Data Structures
Series
DataFrame
Missing Values
Data Operations
Data Standardization
Pandas File Read and Write Support
Data Acquisition (Import & Export)
Selection
Filtering
Combining and Merging Data Frames
Normalization method
Removing Duplicates & String Manipulation
Introduction to Data Visualization
Python Libraries
Plots
Matplotlib Features
Line Properties Plot with (x, y)
Controlling Line Patterns and Colors
Set Axis
Labels
and Legend Properties
Alpha and Annotation
Multiple Plots
Subplots
Seabo
Regression Problem Analysis
Mathematical modeling of Regression Model
Gradient Descent Algorithm
Programming Process Flow
Use cases
Programming Using python
Building simple Univariate Linear Regression Model
Multivariate Regression Model
Boston Housing Prizes Prediction
Cancer Detection Predictive Analysis
Best Fit Line and Linear Regression
Neurons
ANN & Working
Single Layer Perceptron Model
Multilayer Neural Network
Feed Forward Neural Network
Cost Function Formation
Applying Gradient Descent Algorithm
Backpropagation Algorithm & Mathematical Modelling
Programming Flow for backpropagation algorithm
Use Cases of ANN
Programming SLNN using Python
Programming MLNN using Python
Digit Recognition using MLNN
XOR Logic using MLNN & Backpropagation
Diabetes Data Predictive Analysis using ANN
Hierarchical Clustering
K Means Clustering
Use Cases for K Means Clustering
Programming for K Means using Python
Image Color Quantization using K Means Clustering Technique
Clustering
Dimensionality Reduction
Data Compression
Concept and Mathematical modeling
Use Cases
Programming using Python
IRIS Data Analysis using PCA
Understand limitations of A Single Perceptron
Understand Neural Networks in Detail
Backpropagation : Learning Algorithm
Understand Backpropagation : Using Neural Network Example
Why Deep Learning?
SONAR Dataset Classification
What is Deep Learning?
Feature Extraction
Working of a Deep Network
Training using Backpropagation
Variants of Gradient Descent
Types of Deep Networks
Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN
Transfer Learning and Fine-tuning Convolutional Neural Networks
Image classification using Keras deep learning library
Machine Learning
GoLogica’s training on Machine Learning will provide all the necessary training to become a certified Data Scientist with Proficiency in Python. Learn different algorithms like supervised, unsupervised and reinforcement algorithms. Get hands-on experience on statistical techniques, ML model, ML algorithms, focus on deep learning, basics of regression algorithms, SVM and Improving Performance, etc.
WEEK 1-3 30 Hours LIVE CLASS
Data Science with Python
GoLogica provides Data Science with Python Training This course has been designed with a focus on quality and simplicity making it ideal for Beginners or for those looking for a refresher on Data Science with Python. It gives an engaging learning experience covering everything you need to know about Data Science.
WEEK 3-4 30 Hours LIVE CLASS
Your first program
Types
Expressions and Variables
String Operations
Lists and Tuples
Sets
Dictionaries
Conditions and Branching
Loops
Functions
Objects and Classes
Reading files with open
Writing files with open
Loading data with Pandas
Working with and Saving data with Pandas
Applied Data Science with Python
GoLogica provides online lessons to help you get better and advance in your work. Join our Python data science course to learn Python skills, work with data, and get better at stats and machine learning. Make cool pictures too Learn from teachers who know a lot, work on real projects, and grow in the world of data science. Come with us now for a practical learning session.
WEEK 3-4 30 Hours LIVE CLASS
Python Data Science-Centric Libraries
NumPy
NumPy Arrays
Select NumPy Operations
SciPy
pandas
Creating a pandas DataFrame
Fetching and Sorting Data
Scikit-learn
Matplotlib
Seaborn
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
What is Data Science?
Data Science, Machine Learning, AI?
The Data-Related Roles
The Data Science Ecosystem
Tools of the Trade
Who is a Data Scientist?
Data Scientists at Work
Examples of Data Science Projects
An Example of a Data Product
Applied Data Science at Google
Data Science Gotchas
Typical Data Processing Pipeline
Data Discovery Phase
Data Harvesting Phase
Data Priming Phase
Exploratory Data Analysis
Model Planning Phase
Model Building Phase
Communicating the Results
Production Roll-out
Data Logistics and Data Governance
Data Processing Workflow Engines
Apache Airflow
Data Lineage and Provenance
Apache NiFi
Descriptive Statistics
Non-uniformity of a Probability Distribution
Using NumPy for Calculating Descriptive Statistics Measures
Finding Min and Max in NumPy
Using pandas for Calculating Descriptive Statistics Measures
Correlation
Regression and Correlation
Covariance
Getting Pairwise Correlation and Covariance Measures
Finding Min and Max in pandas DataFrame
Repairing and Normalizing Data
Dealing with the Missing Data
Sample Data Set
Getting Info on Null Data
Dropping a Column
Interpolating Missing Data in pandas
Replacing the Missing Values with the Mean Value
Scaling (Normalizing) the Data
Data Preprocessing with scikit-learn
Scaling with the scale() Function
The MinMaxScaler Object
Data Visualization
Data Visualization in Python
Matplotlib
Getting Started with matplotlib
The matplotlib.pyplot.plot() Function
The matplotlib.pyplot.bar() Function
The matplotlib.pyplot.pie () Function
Subplots
Using the matplotlib.gridspec.GridSpec Object
The matplotlib.pyplot.subplot() Function
Figures
Saving Figures to a File
Seaborn
Getting Started with seaborn
Histograms and KDE
Plotting Bivariate Distributions
Scatter plots in seaborn
Pair plots in seaborn
Heatmaps
ggplot
Types of Machine Learning
Terminology: Features and Observations
Representing Observations
Terminology: Labels
Terminology: Continuous and Categorical Features
Continuous Features
Categorical Features
Common Distance Metrics
The Euclidean Distance
What is a Model
Supervised vs Unsupervised Machine Learning
Supervised Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Choosing the Right Algorithm
The scikit-learn Package
scikit-learn Estimators, Models, and Predictors
Model Evaluation
The Error Rate
Confusion Matrix
The Binary Classification Confusion Matrix
Multi-class Classification Confusion Matrix Example
ROC Curve
Example of an ROC Curve
The AUC Metric
Feature Engineering
Scaling of the Features
Feature Blending (Creating Synthetic Features)
The 'One-Hot' Encoding Scheme
Example of 'One-Hot' Encoding Scheme
Bias-Variance (UnderfittingvsOverfitting) Trade-off
The Modeling Error Factors
One Way to Visualize Bias and Variance
UnderfittingvsOverfitting Visualization
Balancing Off the Bias-Variance Ratio
Regularization in scikit-learn
Regularization, Take Two
Dimensionality Reduction
PCA and isomap
The Advantages of Dimensionality Reduction
The LIBSVM format
Life-cycles of Machine Learning Development
Data Splitting into Training and Test Datasets
ML Model Tuning Visually
Data Splitting in scikit-learn
Cross-Validation Technique
Hands-on Exercise
Classification (Supervised ML) Examples
Classifying with k-Nearest Neighbors
k-Nearest Neighbors Algorithm
Hands-on Exercise
Regression Analysis
Regression vs Correlation
Regression vs Classification
Simple Linear Regression Model
Linear Regression Illustration
Least-Squares Method (LSM)
Gradient Descent Optimization
Multiple Regression Analysis
Evaluating Regression Model Accuracy
The R2 Model Score
The MSE Model Score
Logistic Regression (Logit)
Interpreting Logistic Regression Results
Decision Trees and Terminology
Properties of Decision Trees
Decision Tree Classification in the Context of Information Theory
The Simplified Decision Tree Algorithm
Using Decision Trees
Random Forests
Hands-On Exercise
Support Vector Machines (SVMs)
Naive Bayes Classifier (SL)
Naive Bayesian Probabilistic Model in a Nutshell
Bayes Formula
Classification of Documents with Naive Bayes
Unsupervised Learning Type: Clustering
Clustering Examples
k-Means Clustering (UL)
k-Means Clustering in a Nutshell
k-Means Characteristics
Global vs Local Minimum Explained
Hands-On Exercise
XGBoost
Gradient Boosting
Hands-On Exercise
A Better Algorithm or More Data?
What is Python?
Additional Documentation
Which version of Python am I running?
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
Python Variables and Basic Syntax
Variable Scopes
PEP8
The Python Programs
Getting Help
Variable Types
Assigning Multiple Values to Multiple Variables
Null (None)
Strings
Finding Index of a Substring
String Splitting
Triple-Delimited String Literals
Raw String Literals
String Formatting and Interpolation
Boolean and Boolean Operators
Numbers
Looking Up the Runtime Type of a Variable
Divisions
Assignment-with-Operation
Comments:
Relational Operators
The if-elif-else Triad
An if-elif-else Example
Conditional Expressions (a.k.a. Ternary Operator)
The While-Break-Continue Triad
The for Loop
try-except-finally
Lists
Main List Methods
Dictionaries
Working with Dictionaries
Sets
Common Set Operations
Set Operations Examples
Finding Unique Elements in a List
Enumerate
Tuples
Unpacking Tuples
Functions
Dealing with Arbitrary Number of Parameters
Keyword Function Parameters
The range Object
Random Numbers
Python Modules
Importing Modules
Installing Modules
Listing Methods in a Module
Creating Your Own Modules
Creating a Runnable Application
List Comprehension
Zipping Lists
Working with Files
Reading and Writing Files
Reading Command-Line Parameters
Accessing Environment Variables
What is Functional Programming (FP)?
Terminology: Higher-Order Functions
Lambda Functions in Python
Example: Lambdas in the Sorted Function
Other Examples of Using Lambdas
Regular Expressions
Using Regular Expressions Examples
Python Data Science-Centric Libraries
To become a master in Artificial Intelligence Engineer?
Skills Covered
Tools Covered
Career Support
Personalized Industry Session
This will help you to better understand the Artificial Intelligence industry.
High-Performance Coaching
you will be able to grow your career by broadening your proficiency in Artificial Intelligence Engineer.
Career Mentorship Sessions
With this, the students will be able to decide their careers in the right way.
Interview Preparation
We Help with face-to-face interaction through mock interviews & Exams
Program Fee
Program Fee: 67000 /-
60300 /-
Discount: 6700
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Artificial Intelligence Engineer Certification
GoLogica Artificial Intelligence Engineer Certification holds accreditation from major global companies worldwide. Upon completion of both theoretical and practical sessions, we offer certification to both freshers and corporate trainees. Our certification on Artificial Intelligence Engineer is recognized globally through GoLogica, significantly enhances the value of your resume, opening doors to prominent job positions within leading MNCs. Attainment of this certification is contingent upon the successful completion of our training program and practical projects.
Job Outlook
Career Opportunities & Annual Growth
The U.S. Bureau of Labor Statistics forecasts a 42% increase in employment for Artificial Intelligence Engineer analysts from 2020 to 2027, significantly outpacing the average for all occupations. Additionally, Artificial Intelligence Engineer Ventures predicts 2.4 million unfilled Artificial Intelligence jobs worldwide by 2030.
Salary Trend
According to the BLS, Artificial Intelligence Engineer professionals are well-compensated. The median annual wage for Artificial Intelligence Engineer Specialist was $90,000 to $150,000 PA. It’s depending on factors such as experience, location, and specific job responsibilities.
Job Titles
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AI Engineer
Machine Learning Engineer
Data Scientist
Research Scientist
AI Architect
Business Intelligence Developer
Robotics Engineer
AI Product Manager
AI Consultant
Artificial Intelligence Engineer Faq’s
• Learn the concept, phases, implementations, impacts, and many more.
• Basic programming techniques and features. It includes data types, basic operators, tuples, data types, etc.
• How to develop Python programs and make use of the Jupyter Notebook for analyzing basic data.
• How to use the SciPy and NumPy packages for high-level technical and mathematical.
• Artificial neural networks, high-level NLP interfaces, and training deep RNN networks.
• Data Analysts.
• Undergraduates and Fresher’s.
• Individuals who want to begin their career as an AI engineer.
• Software Engineers.
• Business Intelligence Professionals.
It is a training program, which helps the students to learn about artificial intelligence in detail. Also, will help you learn how to develop work-ready AI skills.
Joining NLP courses is not necessary. However, these courses can benefit you in your career as an AI engineer. NLP or natural language processing is a vital subfield of AI, which understands and processes human language. With this course, you will gain specialized knowledge and techniques for developing advanced AI models to understand and generate human knowledge.
• Deep dive into the purposes, phases, concepts, domains, implementations, breadth, and impacts of artificial intelligence.
• Attain proficiency in basic programming features & technical aspects. It includes tuples, functions, arrays, functions, data types, lists, and basic operators.
• Experience in data exploration, hypothesis formulation, data wrangling, data visualization, and testing techniques in Data Science.
• Knowledge of core functions, principles, execution pipeline, and operations of TensorFlow.
And many more.
It helps you become an expert in artificial intelligence. With this program, you will acquire knowledge and practical skills for understanding and implementing several AI technologies, concepts, and algorithms. The Master’s program includes machine learning, computer vision, natural language processing, deep learning, and many more.
Having knowledge of Python programming and a basic understanding of statistics is essential for students joining this Master’s program. This will help us understand the basic concepts of AI and ML.
This course will help you learn the principles of statistics for Python programming, feature engineering, machine learning, and data visualization. With this, you will learn how to make use of Python libraries such as Scikit-learn, TensorFlow, and Matplotlib. This course will also teach you machine learning techniques such as unsupervised and supervised learning. Also, will help you learn advanced concepts like layers of data, TensorFlow, artificial neural networks, and feature extraction.
No! Coding knowledge is not necessary to do this Master’s program.
The salary range for AI engineers varies. It depends on various factors such as industry, location, experience, and many more.
It varies depending on the knowledge & experience of an individual in the field. The program is designed for freshers and individuals with a basic knowledge of this field. Thus, it would be good if you have knowledge or background in programming, computer science, and mathematics for better learning experience.
Yes! A graduate degree is needed. It would be good if you have a background in mathematics, computer science, etc. Also, having basic technical skills is enough to join this course.
• Online AI engineer Master’s program offers convenience and flexibility as compared to offline ones.
• Online courses simplify networking via virtual discussions & online communities. However, offline programs offer wide access to digital resources.
• Online programs are done through virtual classrooms and digital platforms. However, offline programs consist of attending workshops, and physical classes at a specific place.
Having basic statistical knowledge and Python programming concepts is good. This will help the students to understand the AI and machine learning concepts.
You can finish the certification course in 4 to 6 months.
Design, develop, and implement AI systems & algorithms. An AI engineer has to develop machine learning models, optimize algorithms, and evaluate data for certain tasks. They collaborate with software engineers and data scientists to develop intelligent systems to learn, acclimate, and make decisions.
It provides a comprehensive curriculum to provide students with an in-depth understanding of AI theories, applications, concepts, and algorithms. Once you complete this Master’s program, you will get a detailed understanding of AI. You can make use of this knowledge for developing real-world games, projects, logic restraint satisfaction concerns, prediction models, and many more. Also, will help you learn about the vital aspects of data science.
This course will help you attain a competitive edge over your peers and develop job-ready skills. The classes are taken by top-notch industry experts with rich domain experience. With this course, you will get to know about several AI concepts like computer vision, neural networks, machine learning, deep learning, natural language processing, etc.
