English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 39m | 673 MB
Gain an in-depth understanding of data analysis with various Python packages
Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science.
In this course you will learn all the necessary libraries that make data analytics with Python a joy.You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.
By the end of this course, you’ll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science. Welcome to our journey!
This course consists of examples giving it a practical approach with a detailed explanation to the concepts. Lectures are followed by hands-on coding where you’ll learn how to code in Python by using real-world datasets. This way, you will have the chance to repeat the process and compare your coding and results with the ones provided by the lecturer. This will enable you to practice the knowledge you’ve gained with each video.
What You Will Learn
- Explore hands-on data analysis and machine learning by coding in Python
- Become proficient in working with real life data collected from different sources such as CSV files, websites, and databases
- Get hands on with the Numpy for numerical and scientific computation.
- Learn about pre-processing data to make it ready for data analysis
- Carry out visualization with the Matplotlib, and Seaborn libraries
- Understand exploratory data analysis, summarizing data, and creating statistics out of data with Pandas
- Implement Machine Learning algorithms and delve into various machine learning techniques, and their advantages and disadvantages
- Work with regression, classification, clustering, supervised and unsupervised machine learning, and much more!
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