MP4 | Video: h264, 1280x720 | Audio: AAC, 48000 Hz
Language: English | Size: 5.03 GB | Duration: 12h 33m

What you'll learn
Basic syntax in Python and learn how to use it for data analysis.
Practical use of Numpy and pandas, learning how to handle data, especially how to deal with null values, imbalanced data, and so on.
Data visualization techniques with matDescriptionlib and Seaborn as well as other advanced visualization tools.
Pre-processing techniques for binary classification, multi-class classification, and regression.
Scikit-learn basics with supervised and unsupervised algorithms and modules
8 key hands-on practices for supervised and unsupervised data analytics tasks
Image classification techniques with TensorFlow
Basic concepts and practices for anomaly detection, GANs, and NLP.
Requirements
No prior experience is required. This course is for absolute beginners, so it might be too easy for someone already familiar with data science and machine learning techniques.
The only things you need are an internet connection and a Google account since this course will use the Google Colab website, which is free to use. You do not have to install anything. The only things you have to do are (1) download our ipynb notebooks and upload them to your Google Colab account (2) download the data we provide and upload it to your Google Colab account, (3) work on those notebooks, and (4) watch the videos to learn more.
Description
The course is the easiest training you can get to start your journey as a data scientist. We help you prepare your portfolio by having you do tabular data classifications, image classifications, and more during eight mini-projects. Learning Python, NumPy, pandas, matDescriptionlib, Seaborn, TensorFlow, or PyTorch is good, but working on real projects for data science is essential.

A lot of classes talk about mathematics and advanced statistics, but those things are not easy for an absolute beginner to learn. This course won't teach you math or statistics. Instead, we'll guide you through practical applications.

We are not saying math and statistics are not important. It is imperative that you understand those concepts to become a data scientist. But when you first learn how to drive, you don't need someone to teach you the mechanical details of how your car runs. What you need is someone who can sit next to you and guide you from your home to school or the grocery store. If you get familiar with that, then you will want to go further. In the same way, after you do our projects step-by-step, you will want to know more about the math or statistics behind all that code.

We will start with Python, and then NumPy & pandas for some data manipulation. Then we will learn data visualization, preprocessing, machine learning, and modeling for binary classification with tabular data. Next, we will learn regression and multi-class classification with scikit-learn modules. After that, we'll learn how to deal with unsupervised learning tasks like image classification, image generation, and so on.

Who this course is for:
Absolute beginners who are interested in data science or becoming data scientists

Screenshots



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