Solve Real World Business Problems with AI Solutions, Learn Data Science, Data Analysis, Machine Learning (Artificial In
What you will learn
Build a portfolio of work to have on your resume
Developer Environment setup for Data Science and Machine Learning
Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
Real life case studies and projects to understand how things are done in the real world
Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
Why take this course?
This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andreiβs courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery!
Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).
This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.
The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!
The topics covered in this course are:
– Data Exploration and Visualizations
– Neural Networks and Deep Learning
– Model Evaluation and Analysis
– Python 3
– Tensorflow 2.0
– Numpy
– Scikit-Learn
– Data Science and Machine Learning Projects and Workflows
– Data Visualization in Python with MatPlotLib and Seaborn
– Transfer Learning
– Image recognition and classification
– Train/Test and cross validation
– Supervised Learning: Classification, Regression and Time Series
– Decision Trees and Random Forests
– Ensemble Learning
– Hyperparameter Tuning
– Using Pandas Data Frames to solve complex tasks
– Use Pandas to handle CSV Files
– Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
– Using Kaggle and entering Machine Learning competitions
– How to present your findings and impress your boss
– How to clean and prepare your data for analysis
– K Nearest Neighbours
– Support Vector Machines
– Regression analysis (Linear Regression/Polynomial Regression)
– How Hadoop, Apache Spark, Kafka, and Apache Flink are used
– Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
– Using GPUs with Google Colab
By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.
Hereβs the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you donβt know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don’t really explain things well enough for you to go off on your own and solve real life machine learning problems.
Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you donβt know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.
Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.
You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!
Click βEnroll Nowβ and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!
Overview
Iβve spent over a decade in the software engineering trenches, and if thereβs one thing Iβve learned, itβs that the “tutorial hell” is real. Most courses give you a shiny toy version of Data Science that breaks the moment you try to apply it to a messy, real-world project. This “Data Science Masterclass Hands-on ML & AI Projects” caught my eye because it doesn’t just treat Machine Learning as a series of math equations; it treats it as a full-stack engineering discipline.
The core philosophy here is building a portfolio of work that actually means something to a hiring manager. We aren’t just talking about the Titanic dataset for the millionth time. This course dives into the “plumbing” of Artificial Intelligenceβthe stuff most instructors skip because it’s hard to teach. It covers everything from setting up a robust developer environment to understanding how Big Data flows through a system. It bridges the gap between a data analyst who can make a chart and a Machine Learning Engineer who can deploy a predictive model that adds actual ROI to a business. This is about career growth for those who want to be more than just “competent” with a Jupyter notebook.
Prerequisites
Don’t let the “beginner to advanced” tag fool youβyou need to bring some baseline skills to the table if you want to keep up. While the course walks you through the developer environment setup, you should have a solid grasp of Python programming fundamentals. If you don’t know what a list comprehension is or how a dictionary works, pause and go learn that first. A basic understanding of high school level statistics and linear algebra will also save you from a lot of headaches when you reach the Deep Learning modules. You don’t need to be a math wizard, but you shouldn’t be afraid of a few matrices.
Skills & Tools
The tech stack covered here is what Iβd call the “Industry Standard” for 2024 and beyond. Itβs not just a toy box; itβs an enterprise-grade toolkit. Youβll be getting your hands dirty with:
- TensorFlow 2.0: Mastering Neural Networks and Transfer Learning using the latest framework standards.
- Big Data Architecture: Learning how Hadoop and Spark handle massive datasets that would crash a standard laptop.
- Data Streaming: Integrating Kafka for real-time data processingβa skill that is currently in high demand for data engineering roles.
- Machine Learning Libraries: Scikit-Learn, Pandas, and NumPy for data manipulation and predictive modeling.
- Data Visualization: Transforming raw numbers into business intelligence insights that stakeholders actually care about.
Career Benefits & Job Roles
If you’re looking for certification prep that actually leads to a paycheck, this is a strong contender. The focus on job-ready skills means you aren’t just memorizing definitions; you’re solving real-world business problems. Completing this course puts you in a prime position for roles like Data Scientist, AI Specialist, or Machine Learning Engineer.
Moreover, the inclusion of data engineering tools like Spark and Kafka opens doors to Big Data Developer positions, which often command higher salaries than entry-level analyst roles. Having a portfolio of work on your resume that demonstrates you can handle the entire lifecycle of an AI projectβfrom data ingestion to model deploymentβis how you survive a technical interview in a competitive market.
Pros
- Holistic Approach: It covers the “unsexy” but vital parts of the job, like data engineering and environment configuration, which most courses ignore.
- Hands-on Labs: The real-life case studies ensure you’re applying theory immediately. You aren’t just watching videos; you’re building AI solutions.
- Modern Stack: Using TensorFlow 2.0 and focusing on Transfer Learning keeps you relevant in an industry that moves at light speed.
- Portfolio-First Mindset: Every project is designed to be a showcase piece for your career growth and resume building.
Cons
The sheer volume of information can be overwhelming for a true novice. The jump from basic data analysis to Big Data tools like Hadoop and Kafka is steep, and you might find yourself needing to consult outside documentation to fully grasp the nuances of distributed computing if you haven’t been exposed to it before. Itβs a deep dive, not a shallow splash, so be prepared to put in significant hours outside of just the video lectures.