Machine Learning Practical Workout | 8 Real-World Projects
What you'll learn
- Deep Learning Practical Applications
- Machine Learning Practical Applications
- How to use ARTIFICIAL NEURAL NETWORKS to predict car sales
- How to use DEEP NEURAL NETWORKS for image classification
- How to use LE-NET DEEP NETWORK to classify Traffic Signs
- How to apply TRANSFER LEARNING for CNN image classification
- How to use PROPHET TIME SERIES to predict crime
- How to use PROPHET TIME SERIES to predict market conditions
- How to develop NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
- How to apply NATURAL LANGUAGE PROCESSING to develop spam filder
- How to use USER-BASED COLLABORATIVE FILTERING to develop recommender system
Requirements
- Deep Learning and Machine Learning basics
- PC with Internet connetion
Description
"Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology.
Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more “deep” the network will be, the more complex nonlinear relationships that can be modeled. Deep learning is widely used in self-driving cars, face and speech recognition, and healthcare applications.
The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the projects include but not limited to:
(1) Train Deep Learning techniques to perform image classification tasks.
(2) Develop prediction models to forecast future events such as future commodity prices using state of the art Facebook Prophet Time series.
(3) Develop Natural Language Processing Models to analyze customer reviews and identify spam/ham messages.
(4) Develop recommender systems such as Amazon and Netflix movie recommender systems.
The course is targeted towards students wanting to gain a fundamental understanding of Deep and machine learning models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master deep and machine learning models and can directly apply these skills to solve real world challenging problems."
Who this course is for:
- Data Scientists who want to apply their knowledge on Real World Case Studies
- Deep Learning practitioners who want to get more Practical Assigmetns
- Machine Learning Enthusiasts who look to add more projects to their Portfolio
Instructors
I'm Dr. Ryan Ahmed, professor, engineer, and founder of Stemplicity, where we help people get past the hype and actually build things with AI, Agentic AI, Cloud, and Data Science.
Over the past 10 years, I've taught 1 million learners across 160 countries — 700,000+ enrolled in my Udemy courses, 260,000+ subscribers on the "Prof. Ryan Ahmed" YouTube channel, and 120,000+ on Coursera. I also run corporate AI training for teams at HSBC, RBC, Discover, and Barclays across the US, Canada, and the UK. I held leadership roles at GM, Samsung, and Stellantis in Canada and the U.S., working on electric and autonomous vehicle technologies.
I hold a MASc, PhD, and MBA from McMaster University. I’m also a licensed Professional Engineer and a Stanford-certified program manager with over 60+ published research papers in AI and battery systems.
But credentials are the least interesting part. Here's what I actually believe: "AI is the biggest opportunity of our lifetime", and most people are sitting it out because they think they need to code or have a PhD. You don't. If you're willing to show up and try, I'll help you get good at this.
Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!
Join 3.8M+ learners who study with Ligency.
With a 4.6 instructor rating, >1.1 M reviews, and 126 courses in 12 languages, we help engineers, leaders, and teams master the skills that power today’s AI revolution - then ship real results.
We start where the real world starts: with large language models and the products they power. You’ll learn the foundations of AI and Generative AI (gen AI), then ship production-grade systems - chatbots, copilots, automations, and AI agents. We go deep on LLM engineering: retrieval (RAG), evaluation, observability, safety, and the patterns teams use to run agentic systems at scale.
Our stack is practical and current. You’ll prototype fast with Python, LangChain, and LangGraph; explore models from OpenAI, Gemini, and Claude (including Claude Code); fine-tune and serve with Hugging Face and Ollama; and take it to production on AWS - from Bedrock to event-driven services. Need automation? We wire it together with n8n, clean interfaces, and CI/CD. Along the way you’ll master prompt engineering that holds up under load.
Where this leads: roles that ship. AI Engineer and LLM Engineer for those who love building; platform and MLOps paths for those drawn to reliability at scale; product and leadership tracks for the people moving Agentic AI from slide decks to business outcomes. The through-line is the same: learn fast, build faster, measure everything, iterate.
Start with our best selling course:
AI Coder: Complete Claude Code & Coding Agents Course - build complete products at speed with AI coding agents like Claude Code, Cursor, Copilot and Codex, no coding background required.
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents - a hands-on path from your first prompt to production patterns: 20+ models, RAG, QLoRA fine-tuning, and agents with LangChain/LangGraph.
AI Engineer Agentic Track: The Complete Agent & MCP Course - design, orchestrate, and deploy robust AI agents across OpenAI Agents SDK, CrewAI, LangGraph and MCP.
AI Engineer Production Track: Deploy LLMs & Agents at Scale - scaling patterns for pipelines, monitoring, and enterprise rollout on AWS with Bedrock, Google Cloud Platform, Azure and MLOps.
AI Builder: Create Agents, Voice Agents & Automations in n8n - wire up low-code AI agents, voice agents and business automations in n8n with ElevenLabs, RAG and MCP.
Practical Guide to AI Agents & Agentic AI with Claude Cowork - build six working no-code AI agents on your real tools (Gmail, Slack, Notion, Calendar) with Claude Cowork and MCP.
AI Leader: Generative AI & Agentic AI for Leaders & Founders - a concise playbook for strategy, governance, and ROI with Generative AI and agents.
If your goal is to level up quickly and ship something real, join us. Learn the concepts, touch the tools, build the thing - then take it to users. That’s the Ligency way.