Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
20 Hour(s) 57 Minute(s)
Language
English
Taught by
Sundog Education by Frank Kane, Frank Kane, Sundog Education Team
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
1 views
Course Overview
Complete hands-on deep learning, AI engineering and Generative AI tutorial with data science, Tensorflow, GPT, OpenAI
Master Machine Learning & AI Engineering — From Data Analytics to Agentic AI Solutions
Launch your career in AI with a comprehensive, hands-on course that takes you from beginner to advanced. Learn Python, data science, classical machine learning, and the latest in AI engineering—including generative AI, transformers, and LLM agents / agentic AI.
Why This Course?
Learn by Doing
With over 145 lectures and 21+ hours of video content, this course is built around practical Python projects and real-world use cases—not just theory.
Built for the Real World
Learn how companies like Google, Amazon, and OpenAI use AI to drive innovation. Our curriculum is based on skills in demand from leading tech employers.
No Experience? No Problem
Start from scratch with beginner-friendly lessons in Python and statistics. By the end, you’ll be building intelligent systems with cutting-edge AI tools.
A Structured Path from Beginner to AI Engineer
1. Programming Foundations
Start with a crash course in Python, designed for beginners. You’ll learn the language fundamentals needed for data science and AI work.
2. Data Science and Statistics
Build a solid foundation in data analysis, visualization, descriptive and inferential statistics, and feature engineering—essential skills for working with real-world datasets.
3. Classical Machine Learning
Explore supervised and unsupervised learning, including linear regression, decision trees, SVMs, clustering, ensemble models, and reinforcement learning.
4. Deep Learning with TensorFlow and Keras
Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), using real code examples and exercises.
5. Advanced AI Engineering and Generative AI
Go beyond traditional ML to learn the latest AI tools and techniques:
Transformers and self-attention mechanisms
GPT, ChatGPT, and the OpenAI API
Fine-tuning foundation models
Advanced Retrieval-Augmented Generation (RAG)
LangChain and LLM agents
Designing and building multi-agent systems with the OpenAI Agents SDK
Real-world GenAI projects and deployment strategies
6. Big Data and Apache Spark
Learn how to scale machine learning to large datasets using Spark, and apply ML techniques on distributed computing clusters.
Designed for Career Growth
Whether you're a programmer looking to pivot into AI or a tech professional seeking to expand your skills, this course delivers a complete, industry-relevant education. Concepts are explained clearly, in plain English, with a focus on applying what you learn.
What Students Are Saying
"I started doing your course... and it was pivotal in helping me transition into a role where I now solve corporate problems using AI. Your course demystified how to succeed in corporate AI research, making you the most impressive instructor in ML I've encountered."
— Kanad Basu, PhD
Enroll Today and Build Your Future in AI
Join thousands of learners who have used this course to land jobs, lead projects, and build real AI applications. Stay ahead in one of the fastest-growing fields in tech.
Start your journey today—from Python beginner to AI engineer.
Course Content
- 17 section(s)
- 151 lecture(s)
- Section 1 Getting Started
- Section 2 Statistics and Probability Refresher, and Python Practice
- Section 3 Predictive Models
- Section 4 Machine Learning with Python
- Section 5 Recommender Systems
- Section 6 More Data Mining and Machine Learning Techniques
- Section 7 Dealing with Real-World Data
- Section 8 Apache Spark: Machine Learning on Big Data
- Section 9 Experimental Design / ML in the Real World
- Section 10 Deep Learning and Neural Networks
- Section 11 Generative Models
- Section 12 Generative AI: GPT, ChatGPT, Transformers, Self Attention Based Neural Networks
- Section 13 The OpenAI API (Developing with GPT and ChatGPT)
- Section 14 Retrieval Augmented Generation (RAG,) Advanced RAG, and LLM Agents
- Section 15 Agentic AI Patterns and the OpenAI Agents SDK
- Section 16 Final Project
- Section 17 You made it!
What You’ll Learn
- Build generative AI systems with OpenAI, RAG, and LLM Agents
- Build artificial neural networks with Tensorflow and Keras
- Implement machine learning at massive scale with Apache Spark's MLLib
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
Skills covered in this course
Reviews
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SShyamasravani Saripalli
Thanks for such a mind blowing and clear hands on and I have loved this practical way and was more clear about these topics and this would be easier to refer in future as well!
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NNikita Shymanski
Frank is superior in describing hard math, calculus and statistics in simple and clear words with python examples and hands-on demos. This course extensively covers Deep Learning, ML algorithms and all possible problems with choosing models, data preparation, training and tuning them, finally validating - to the fullest extent. Keep up the good work, Frank!
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DDavid Zeryck
Frank set up a great build from the foundations of statistical analysis to GenAI. Excellent examples along the way. During the lessons from basic stats to ML and GenAI, he goes deep enough to feel like you can use these tools (with a bit of help from ChatGPT here and there for newer models etc.). One issue is that models change quickly and setting up the libraries for the course was tricky at times. The Q&A team came through though and I was able to run all the exercises (still working on the final bonus tensorflow example, as KerasClassifier is deprecated, lol)
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SSanyam Choudhary
Till now I have covered section 1 and 2. Section 1 - Section 1 was basic and it was ok. Section 2 - on Section 2 as you will move further with videos you will feel tutor is just reading documents / notebook and not using any teaching effort. no one can explain section 2 worse then this . Section 3 - Just completed section 3 , and I am thinking tutor himself is not getting what he is trying to teach, I would request tutor to stop teaching something if he don't know how to explain it, it is waste of time for him and for us. Section 4 to Section 9 - More and More you go further in this course you will realize tutor do not know how to teach, it would be better to get topic name from here and search it in google / ChatGpt to learn it better . I am not sure people who gave 4 or 5 rating are genuine or not, it is not a course it is PPT reading. Section 10 - Completed it , I think tutor has knowledge but he don't know how to explain and teach. Now will be moving further with section 10 to 17 , will update My rating and comment accordingly.
