Data Scientist, Machine Learning and Generative AI Engineer

Total Duration 140 Hours

Level Beginner / Intermediate

Delivery Mode

Public Classroom

Available Schedules

Skills you'll learn

22 skills

Data Visualization

Statistical Analysis

Machine Learning Modeling

Hyperparameter Tuning

Stream lit Development

Fast API Development

MLflow Model Management

MLOps Fundamentals

Deep Learning

Neural Networks

Prerequisites

Basic Python programming knowledge

Basic knowledge of databases and SQL concepts is recommended.

Basic computer literacy and experience working with software applications.

Course Outline

This isn't just a course. It's a structured path designed to turn you into a specialist ready for the global job market.

AI vs ML vs Data Science
ML Workflow
Performance Metrics

Descriptive statistics
Probability & distributions
Hypothesis testing
Linear algebra basics

Python review
Data structures
algorithms
Working with files
Web scraping

SELECT
ORDER BY
WHERE
LIMIT
Aggregation
GROUP BY
COUNT / SUM / AVG / MIN / MAX
JOINs
Subqueries
CASE WHEN
Simple window functions
Extract dataset for ML project

Target Audience

Designed to meet the needs of learners at different career stages, empowering them to build user-centered digital experiences

Developers aspiring to be an ‘Artificial Intelligence Engineer’ or Machine Learning engineers.

Analytics managers who are leading a team of analysts

Information architects who want to gain expertise in Artificial Intelligence algorithms.

Graduates looking to build a career in Artificial Intelligence and Machine Learning

Learning outcomes

What you'll learn

Understanding basics of Artificial Intelligence, Machine Learning, Data Science, and Generative AI and their business applications.

Using statistics, probabilities, and linear algebra to drive decision making.

Use Python programming, SQL queries, and data extraction methods to build datasets for analytics and machine learning.

Clean, analyze, and visualize the data using such popular tools as NumPy, Pandas, Matplotlib, and Seaborn.

Apply advanced machine learning techniques including ensemble learning, boosting algorithms, feature engineering, and hyperparameter optimization

Create interactive machine learning applications using Streamlit and deploy predictive services with Fast API.

Manage machine learning experiments, model tracking, and versioning using ML flow and ML Ops best practices.

Deploy machine learning systems by connecting models, APIs, and applications.

Design, train, and tune deep learning models using Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).

Create computer vision applications using image classification and object detection with modern architecture like YOLO.

Create Retrieval-Augmented Generation (RAG) models which integrate vector search, embeddings, and LLMs to generate contextually accurate answers.

Create AI powered workflows through APIs, LLMs, and business systems by using n8n.

Complete practical projects on Machine Learning, Computer Vision, Generative AI, and AI Automation to create a professional portfolio.

About this course

In the Data Scientist, Machine Learning & Generative AI Engineer ,you will learn how to develop end-to-end AI, machine learning, deep learning, computer vision, natural language processing, and generative AI solutions using industry-standard technologies and tools including Python, SQL, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, Streamlit, Fast API, ML flow, TensorFlow/Keras, YOLO, Hugging Face, Lang Chain, Retrieval Augmented Generation (RAG), vector databases, n8n, and Large Language Models (LLMs).

Participants of this program will be able to master practical skills in such areas as machine learning engineering, deep learning, predictive modeling, computer vision, natural language processing, AI automation, and Generative AI application development.

Data Scientist, Machine Learning & Generative AI Engineer FAQs

What will I learn in the Data Scientist, Machine Learning and Generative AI Engineer program?

You will build AI, machine learning, deep learning, computer vision, NLP and generative AI solutions using Python, SQL, NumPy, Pandas, Scikit-Learn, Streamlit, FastAPI, MLflow, TensorFlow/Keras, YOLO, Hugging Face, LangChain, RAG, vector databases and large language models.

How long is the program and how is it delivered?

The program runs for 140 hours at a beginner to intermediate level in a public classroom.

What do I need before joining?

Basic Python programming knowledge and basic computer literacy are required, and basic knowledge of databases and SQL concepts is recommended.

Does the course include generative AI and LLMs?

Yes. Later modules cover LLM fundamentals, prompt engineering, Hugging Face, retrieval augmented generation (RAG) and LangChain.

What jobs can this program lead to?

Career paths listed include machine learning engineer, generative AI engineer, deep learning engineer, NLP engineer, computer vision engineer, AI solutions developer, MLOps engineer and data engineer.

Accredited Credentials

Attendance Certificate from CLS Learning Solutions

Data Scientist, Machine Learning and Generative AI Engineer

Potential Career Paths

Machine Learning Engineer

Generative AI Engineer

Data Analyst

Deep Learning Engineer

NLP Engineer (Natural Language Processing Engineer)

Computer Vision Engineer

AI Solutions Developer

MLOps Engineer

Data Engineer

Frequently Asked Questions

Find answers to common questions about the course, schedules, and learning process.

Data Scientist, Machine Learning and Generative AI Engineer is a practical training course at CLS designed to build job-relevant skills in data science. It combines expert-led instruction, guided practice and applied learning so participants can understand the core concepts, use the relevant tools and prepare for real professional tasks. The detailed syllabus, duration, delivery options, learning outcomes and prerequisites are listed on this course page.