CLS
Data Scientist, Machine Learning and Generative AI Engineer

Data Scientist, Machine Learning and Generative AI Engineer

Total Duration 140 Hours
Level Beginner / Intermediate
Delivery Mode
Public Classroom
Available Schedules
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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

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.

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