This is a top-selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2022. Become a complete Data Scientist and Machine Learning engineer. Join a live online community of 600,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + 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 This course is focused on efficiency: never spend time on confusing, out-of-date, incomplete Machine Learning tutorials anymore.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 about 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). Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence), and Python with Tensorflow, Pandas & more.
What you’ll learn:
- Become a Data Scientist and get hired.
- Master Machine Learning and use it on the job.
- Deep Learning, Transfer Learning, and Neural Networks using the latest Tensorflow 2.0.
- Use modern tools that big tech companies like Google, Apple, Amazon, and Facebook use.
- Present Data Science projects to management and stakeholders.
- Learn which Machine Learning model to choose for each type of problem.
- Real-life case studies and projects to understand how things are done in the real world.
- Learn best practices when it comes to Data Science Workflow.
- Implement Machine Learning algorithms.
- Learn how to program in Python using the latest Python 3.
- How to improve your Machine Learning Models.
- Learn to pre-process data, clean data, and analyze large data.
- Build a portfolio of work to have on your resume.
- Developer Environment setup for Data Science and Machine Learning.
- Supervised and Unsupervised Learning.
- Machine Learning on Time Series Data.
- Explore large datasets using data visualization tools like Matplotlib and Seaborn.
- Explore large datasets and wrangle data using Pandas.
- Learn NumPy and how it is used in Machine Learning.
- A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided.
- Learn to use the popular library Scikit-learn in your projects.
- Learn about Data Engineering and how tools like Hadoop, Spark, and Kafka are used in the industry
- Learn to perform Classification and Regression modeling.
- Learn how to apply Transfer Learning.
- No prior experience is needed (not even Math and Statistics). We start from the very basics.
- A computer (Linux/Windows/Mac) with an internet connection.
- Two paths for those that know programming and those that don’t.
- All tools used in this course are free for you to use.
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.
- 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.
- Set 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.
Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t 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, 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 don’t 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, the 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.
Who this course is for:
- Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science, and Python.
- You are a programmer that wants to extend your skills into Data Science and Machine Learning to make yourself more valuable.
- Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field.
- You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry.
- You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”.
- You want to learn to use Deep Learning and Neural Networks with your projects.
- You want to add value to your own business or the company you work for, by using powerful Machine Learning tools.