GoLogica offers a Machine Learning training course in Alpharetta City (USA), designed for learners with fundamental skills and tools to excel in AI and data-driven knowledge. The course covers supervised, unsupervised, and reinforcement studying, the side of using Python, and TensorFlow. Key skills consist of professional-led periods, real-world initiatives, enterprise-relevant curriculum, and career assistance.
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+
Our Alumini
Machine Learning Program Details
GoLogica presents an industry-experts Machine Learning training in Alpharetta City, USA, designed to provide learners with the skills and knowledge needed to excel in the fast-evolving field of Artificial intelligence. This complete course introduces individuals to machine learning algorithms, statistical modules, and tools that allow computer systems to research from information and make Data-driven predictions or decisions without express programming.
The training starts evolving by covering essential getting to know paradigms of supervised learning, unsupervised studying, and reinforcement mastering. Supervised learning teaches modules to predict the effects of the usage of labeled datasets for responsibilities like class and regression, while unsupervised mastering uncovers hidden patterns and systems in unlabelled facts through the usage of techniques like clustering and dimensionality reduction. Reinforcement mastering, alternatively, makes a specialty of education structures to make sequential decisions by rewards and consequences.
The course delves into the mathematical foundations of machine learning, consisting of linear algebra, The course of study, opportunity, and data, ensuring beginners have strong theoretical knowledge. Participants will benefit from practical experience using algorithms and modules along with linear regression, logistic regression, a support vector machine (SVM), and neural networks. The most important part of the process is the practical implementation of theoretical knowledge., enabling learning to work with real-world datasets and the usage of programming tools and libraries like Python, NumPy, pandas, and TensorFlow.
This practical publicity guarantees participants are activity-equipped and might with a bit of luck construct and install gadget getting to know modules. The direction additionally emphasizes crucial components of information guidance, which include statistics preprocessing, characteristic engineering, and model evaluation.
Techniques that include normalization, dealing with lacking statistics and go-validation are required to build robust, high-appearing models. Participants are also introduced to superior topics like ensemble learning, deep learning, and natural language processing (NLP) to explore advanced machine learning modules. Additionally, the course addresses demanding situations which include overfitting, the bias-variance change-off, and techniques for improving model performance.
To support assigned and ethical artificial intelligence practices, ethics in machine learning such as providing equity, improving biases, and boosting transparency are explored. To show the practical impact of machine learning across sectors, practical use cases are presented in categories such as autonomous systems, recommendation systems, and image recognition.
By the end of the training, individuals will increase a deep knowledge of machine learning to know ideas, tools, strategies, and certification, empowering them to key roles that include machine learning engineer, Data scientist, or AI expert. The GoLogica Machine Learning course in Alpharetta City ensures a blend of theoretical knowledge and self based training, making it ideal for professionals and beginners a rewarding career in AI and machine learning.
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Machine Learning Syllabus
Python
Python was mainly developed for emphasis on code readability, and its syntax allows programmer to express concepts in fewer lines of code.Python is considered a scripting, language like Ruby or Perl and is often used for creating Web applications and dynamic Web content.
WEEK 5-6 20 Hours LIVE CLASS
History
Features
Setting up path
working with Python
Variable and Data Types
Operator
If
If- else
Nested if-else
For
While
Nested loops
Break
Continue
Pass
Accessing Strings
Basic Operations
String slices
Function and Methods
Accessing tuples
Operations
Working
Functions and Methods
Accessing list
Operations
Working with lists
Function and Methods
Defining a function
Calling a function
Types of functions
Function Arguments
Anonymous functions
Global and local variables
Importing module
Math module
Random module
Packages
Composition
Printing on screen
Reading data from keyboard
Opening and closing file
Reading and writing files
Functions
Exception
Exception handling
except clause
Try? Finally clause
User Defined Exceptions
Class and object
Attributes
Inheritance
Overloading
Overriding
Data hiding
Match function
Search function
Matching VS Searching
Modifiers
Patterns
Introduction
Architecture
CGI environment variable
GET and POST methods
Cookies
File upload
Connections
Executing queries
Transactions
Handling error
Socket
Socket Module
Methods
Client and server
Internet modules
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 10-11 40 Hours LIVE CLASS
1. Introduction to AI
• What is AI? History and Milestones
• Applications of AI in Real-World Problems
• Ethics and Bias in AI Systems
2. Mathematical Foundations
• Linear Algebra for AI (Vectors, Matrices, Eigenvalues)
• Probability and Statistics (Bayesian Inference, Distributions)
• Calculus (Differentiation, Gradients in Optimization)
3. Programming for AI
• Python for AI Development
• Libraries and Tools (NumPy, Pandas, Matplotlib)
4. Supervised Learning
• Linear and Logistic Regression
• Decision Trees and Random Forests
• Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
• Build an end-to-end AI solution using advanced techniques.
16. Model Optimization
• Hyperparameter Tuning
• Transfer Learning
17. AI Deployment
• Model Serving and APIs (Flask, FastAPI)
• Scaling AI with Cloud Services (AWS, Azure, GCP)
18. Ethics and Regulation in AI
• Explainable AI (XAI)
• Privacy and Data Security in AI Systems
19. Special Topics
• Edge AI and IoT
• Quantum Computing for AI
20. Capstone Project
• Design, build, and deploy a comprehensive AI system addressing a real-world problem.
21. AI Career Pathways
• Roles: Data Scientist, AI Engineer, Researcher
• Building a Strong Portfolio and Networking
22. Portfolio Development
• Structuring AI Projects for Resumes
• Publishing Work (GitHub, Research Papers)
23. Mock Interviews and Assessments
• Technical Interview Preparation
• Behavioral Interview Training
• Comprehensive understanding of AI concepts and applications.
• Practical experience through multiple hands-on projects and a capstone project.
• Readiness for AI-related roles in industry or research.
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 7-8 30 Hours LIVE CLASS
Overview
Life Cycle of Data Science
MapReduce Framework
Importance of Data Science
Tools in Data Science
Need of Python in Data Science
Introduction
Terminologies of statistics
Measures of Centers & Spread
Types of distributions
Overview
Data Types and Variables
Operators
if Statements and lops
Functions in R
Introduction
ML Fundamentals
Learning Technologies
Decision Tree
Random forest classifiers
Deep Learning
Time Series Analysis
ChatGpt
In this course, you will learn about the workings of OpenAI’s ChatGPT model, the model architecture, and the fine-tuning techniques. You will also discover in detail how to embed ChatGPT into various applications, control conversation flows, and fine-tune it for distinct tasks. Applied, you’ll learn about the operative utilization and development of AI chatbots as well as NLP, making ChatGPT your robust tool in AI capability.
WEEK 3-4 12 Hrs LIVE CLASS
What is Chat GPT?
Understanding the capabilities and limitations
Use cases and applications
Setting up the environment
Choosing a development platform
Accessing the API or using the local runtime
Formatting your input
Sending requests to the model
Handling responses
Exploring different input formats
Prompts and instructions
System and user messages
Controlling response length
Adjusting temperature and top-k sampling
Maintaining context
Multi-turn conversations
Conversation history and context management
Resolving ambiguity and clarifying queries
Introduction to fine-tuning
Preparing your dataset
Training your fine-tuned model
Evaluating and deploying your model
Writing effective prompts and instructions
Managing bias and controversial topics
Handling sensitive information and user privacy
Ensuring safety and avoiding harmful outputs
Chat GPT API advanced features
Using system-level instructions for better control
Handling rare and out-of-distribution queries
Incorporating user feedback for model improvement
Python Statistics for Data Science
The GoLogica Python Statistics course offers students to learn what it takes to be a data scientist and the basic statistical methods required. In this course, you will find out about descriptive statistics, probability, inferential statistics, and hypothesis testing. The course involves the use of libraries such as NumPy, Pandas, and SciPy, prevalent techniques in Python for analyzing real data together with statistical methods. By the end, you’ll be able to make sense of your data and build data models using one of the preferred languages for anyone working in data science or machine learning toda —Python.
WEEK 9-10 35 Hours LIVE CLASS
Introduction to Data Types
Numerical parameters to represent data
Mean
Mode
Median
Sensitivity
Information Gain
Entropy
Statistical parameters to represent data
Uses of probability
Need of probability
Bayesian Inference
Density Concepts
Normal Distribution Curve
Point Estimation
Confidence Margin
Hypothesis Testing
Levels of Hypothesis Testing
Testing the DataParametric Test
Parametric Test Types
Non- Parametric Test
Experimental Designing
A/B testing
Association and Dependence
Causation and Correlation
Covariance
Simpson’s Paradox
Clustering Techniques
Logistic and Regression Techniques
Problem of Collinearity
WOE and IV
Residual Analysis
Heteroscedasticity
Homoscedasticity
Pyspark
This course provides guidelines on how to process big data through PySpark, which is a vital instrument in big data processing and compute clusters. You will learn how to manipulate big data using Spark’s data structures, including RDDs and Spark Dataframes. The course also introduces Machine Learning with PySpark to help you build scalable ML models. This certification makes you ready to work on big data technologies professionally.
WEEK 9-10 35 Hrs LIVE CLASS
What is Script?
What is a program?
Types of Scripts
Difference between Script & Programming Languages
Features of Scripting
Limitation of Scripting
Types of programming Language Paradigms
Introduction to data Big Data?
Introduction to NumPY and SciPY
Introduction to Pandas and MatPlotLib
What is Machine learning?
Machine Learning Methods
Predictive Models
Descriptive Models
What are the steps used in Machine Learning?
What is Deep Learning?
What is Data Science?
Data Science Life Cycle?
What is Data Analysis
What is Data Mining
Analytics vs Data Science
IMPACT OF THE INTERNET
What is IOT
History of IoT
What is Network?
What is Protocol?
What is smart?
How IoT Works?
The Future of IoT
To become a master in Machine Learning?
Skills Covered
Tools Covered
Career Support
Personalized Industry Session
This will help you to better understand the Machine learning.
High-Performance Coaching
you will be able to grow your career by broadening your proficiency in Machine learning.
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: 81000 /-
72900 /-
Discount: 8100
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Machine Learning Certification
GoLogica Machine Learning 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 Machine Learning 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
Personalized Industry Session
The U.S. Bureau of Labor Statistics (BLS) forecasts a 22% increase in employment for Machine Learning from 2021 to 2031, significantly outpacing the average for all occupations. Additionally, Machine Learning Ventures predicts 3.5 million unfilled cybersecurity jobs worldwide by 2025.
High-Performance Coaching
According to the BLS, Machine Learning professionals are well-compensated. The median annual wage for Data Engineer was $110,000 and $160,000 PA It’s depending on factors such as experience, location, and specific job responsibilities.
Job Titles
Are you preparing for a interview? If yes, our expert tutors will help you with this.
Machine Learning Engineer
AI Engineer
Data Scientist
ML/AI Research Analyst
ML/AI Consultant
Business Intelligence Analyst
Machine Learning Faq’s
The Machine Learning Master’s Program is a course that consists of all the necessary topics, starting from the basic understanding of machine learning, data science, and AI up to the most complex concepts. The material includes concepts including supervised and unsupervised learning, reinforcement engineering, projects, and real-life use cases.
The program is built for working professionals looking forward to becoming a Machine Learning Engineer, Data Scientist, AI Engineer, or Data Analyst. It is also appropriate for Software engineers, IT personnel, or recent employees desiring to make a career change to be an ML engineer.
The program has several modules that address different areas of Machine Learning, e.g. data preprocessing, model construction, distribution, deeper learning, as well as AI applications. The program also includes real-life projects and a final project.
The course generally takes between 6 and 12 months, depending on your speed and commitment and the several hours you spend per week.
The program is fully online, and live instructor-led courses are delivered as well.
Yes, Python is the principal language used for implementing machine learning algorithms and models.
Some of the positions are AI Engineer, Machine Learning Engineer, ML/AI Consultant, Data Scientist, and Business Intelligence Analyst.
Machine Learning experience is not necessary, though knowledge of Python and statistics is desirable.
The average salary for ML specialists is between 10 lakhs and 12 lakhs annually.
This program provides many-sided learning with lessons and presentations by professionals, practical work, individual consultations, and training in demanded skills.
Career support includes 1:1 mentorship, interview preparation, resume creation, and job suggestion services.
Learners have access to 24/7 support for technical queries, mentorship from ML professionals, and guidance on projects and assignments.
Yes, there are coding-related quizzes as well as coding assignments that are done throughout the program.
Highlights of the program include programming with Python, Big Data with Apache PySpark, Data Science, ChatGPT, and more industry-related technologies.
Yes, the mode of operation can be either part-time or full-time, depending on the user’s schedule.
Yes, you will have lifetime access to all recorded sessions and course materials after the completion of the program.
It is possible to register directly from GoLogica’s website by choosing the Machine Learning Master’s Program and filling out the registration application form.
The tutorials are in the form of assignments and projects, which are graded by the instructors, and you receive feedback for improvement.
International students are welcome to the program, and all lessons are delivered online.
While in live classes, if you did not attend or were unable to attend certain classes, you can be able to view the recorded session at your own time.
Yes, the program includes personalized 1:1 professional sessions with mentors in the industry.
Yes, there is. You will be awarded a Machine Learning Master’s Program certification if you clear the course and the capstone project.
It gives you enough knowledge about how you can build the models and algorithms and implement the solution. They offer practical exercises and a final project that lets you show the skills you have learned to prospective employers.
Yes, you will receive support from tutors and instructors throughout your process of completing the capstone, and they will gladly assist you if you encounter any problems on the way.
Particular classes may allow the students to work in groups on certain assignments; other classes may prefer individual work.
PySpark refers to the use of the Spark programming interface in Python, where you can complete the analysis of large data and create, train, and use machine learning models.
Yes, this course can accommodate beginners who have a desire to learn machine learning and weak programming skills. The concepts in the course are introduced at the basic level and gradually build up to expert levels.
Yes, the program has a flexible schedule; for instance, the classes may be conducted on weekends for working learners.
Yes, the program is designed for working individuals, flexibility in the hours of learning, and the need for part-time learning.
This course gives you the basic and advanced skills required in data science as far as data analysis and building models are concerned to ensure that you make a transition to data science positions.
