Machine Learning & Data Science Bootcamp

Description:

This is a best-selling Machine Learning and Data Science course that was been updated with the most recent trends and abilities for 2023. Develop your skills as a full Data Scientist and Machine Learning engineer. Join a live online community of over 900,000 engineers and a course taught by industry specialists. Who have worked for Fortune 500 firms in Silicon Valley and Toronto. Graduates of Andrei’s classes currently work at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, and other leading technology firms. You will progress from inexperience to mastery!

Who should take Machine Learning & Data Science course:

• Anyone with no experience (or a beginner/junior) who wants to study Machine Learning, Data Science, and Python is welcome to apply.
• You are a programmer who want to broaden your knowledge of Data Science and Machine Learning in order to increase your value.
• Anyone interested in learning about these issues from industry specialists who have not only taught but also worked in the field.
• You’re seeking for a single course that will teach you about Machine Learning and Data Science while also getting you up to speed with the industry.
• You want to study the foundations and genuinely comprehend the topics rather than just watching someone write on your computer for hours and not “getting it.”
• You’d want to learn how to use Deep Learning and Neural Networks to your projects.
• You want to offer value to your own firm or the organization for which you work by utilizing strong Machine Learning techniques.

 This course is all about efficiency: never waste time on confusing, out-of-date, or incomplete Machine Learning lessons again. We are convinced that this is the most thorough and up-to-date education on the subject available anywhere (bold statement, we know).

What Machine Learning Course will teach you:

This thorough and project-based course will teach you to all of the contemporary Data Scientist abilities, and we will construct numerous real-world projects to add to your portfolio along the way. You will have immediate access to all of the code, workbooks, and templates (Jupyter Notebooks) on Github, allowing you to add them to your portfolio! We feel that this course addresses the most significant barrier to entry into the Data Science and Machine Learning fields: having all of the essential materials in one location and learning the current trends and on-the-job abilities that companies need.

The course has two tracks. If you already know how to programme, you may move forward to the section where we teach you Python from the ground up. If you are absolutely new, we will start from scratch and teach you Python and how to utilise it in the real world for our projects. Don’t worry, once we’ve covered the fundamentals like Machine Learning 101 and Python, we’ll move on to more advanced topics like Neural Networks, Deep Learning, and Transfer Learning so you can get real-world practise (we show you fully fledged Data Science and Machine Learning projects and provide programming resources and cheatsheets)!

Machine Learning course covers the following topics:

• Exploration and visualization of data
• Deep Learning and Neural Networks
• Analysis and evaluation of models
• Python version 3
• Tensorflow
• Numpy
• Scikit-Learn
• Projects and Workflows in Data Science and Machine Learning
• Python Data Visualization Using MatPlotLib and Seaborn
• Learning Transfer
• Recognition and categorization of images
• Cross validation and training/testing
• Classification, Regression, and Time Series Supervised Learning
• Random Forests and Decision Trees
• Collective Learning
• Tuning Hyperparameters
• Using Pandas Data Frames to tackle difficult problems
• To work with CSV files, use Pandas
• TensorFlow 2.0 with Keras Deep Learning / Neural Networks
• Entering Machine Learning tournaments with Kaggle
• How to Present Your Findings to Your Boss
• Cleaning and preparing your data for analysis
• Nearest Neighbors K
• Support Vector Machines (SVMs)
• Analysis of regression (Linear Regression/Polynomial Regression)
• How to Use Hadoop, Apache Spark, Kafka, and Apache Flink
• Conda, MiniConda, and Jupyter Notebooks are used to set up your environment.
• Utilizing GPUs in Google Colab

By the end of this course

 you will be a fully-fledged Data Scientist capable of being employed by huge corporations. Everything we learn in the course will be used to professional real-world applications such as Heart Disease Detection, Bulldozer Price Prediction, Dog Breed Image Classifier, and many more. By the conclusion, you will have a collection of projects that you may show off to others.

Click “Enroll Now” to join others in our community in advancing their careers as Data Scientists and Machine Learning. We promise that this is superior to any bootcamp or online course available on the subject. See you inside the classroom!