Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

This Training Program will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and OOP, complete hands-on exercises throughout the Program.

getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

you gain the prerequisite mathematical knowledge to continue your journey and take more advanced Topics in machine learning and Data Science

you will learn to navigate and parse HTML, CSS code, and crawl websites automatically. our scraping will be conducted using the versatile Python library BeautifulSoup many of the techniques you learn in this program can be applied to other popular Python libraries as well, including Scrapy and Selenium. Upon the completion of this program, you will have a strong mental model of HTML structure, will be able to build tools to parse HTML code and access desired information, and create simple scrapy spiders to crawl the web at scale.

a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You’ll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. You will create new tables and be able to move data into them. You will learn common operators and how to combine the data. You will use case statements and concepts like data governance and profiling. You will discuss topics on data, and practice using real-world programming assignments. You will interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape your data for targeted analysis purposes.

working with real-world datasets to retrieve insights from data. You’ll be exposed to different kinds of data structure and data-related problems. You’ll learn how to prepare data for analysis, perform simple statistical analyses, predict future trends from data, and more!

you’ll learn about NumPy and how to work with data using the library. That means digging into concepts such as vectorized operations and going hands-on with selecting data from NumPy ndarrays. To enhance your knowledge of NumPy, you’ll then learn how to select data based on their values using a technique called boolean indexing.

After you learn about NumPy, we’ll jump into pandas, which is the most popular open source library for data science. Along with learning about the pandas library, you’ll learn how it can work with NumPy to supercharge your data analysis workflow.

Once you’ve mastered the basics of NumPy and pandas, you will get some practice exploring data using the pandas library and learning how to select data in a pandas dataframe, how to filter your data, and more pandas analytical techniques. You’ll also learn more data cleaning techniques, using pandas to make your data easier to work with.

Master Matplotlib Anatomy and Learn Seaborn to Visualize Data with Custom, Beautiful Charts, Suitable for All Purposes

Matplotlib and Seaborn are powerful Python library that makes it easy to create informative and attractive visualizations. you will learn how to visualize your data using plots such as scatter plots, box plots, and bar plots.

Understand what data preprocessing is and why it is needed as part of overall data science and machine learning methodology.

data is very messy and comes in a variety of form. As part of the overall data mining and machine learning process, we must take the time to preprocess our data. This means we must ensure that it is structured, cleansed, and address any problems that the data may have. Preprocessing the data includes gaining a better understanding of the data through descriptive statistics and data visualization techniques. It also includes ensuring that missing data or outliers are handled accordingly.

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

you will learn how to extract and identify useful features that best represent your data, machine learning algorithms, (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks, More…). (ii) Unsupervised learning (clustering, dimensionality reduction, More…). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI) and how to evaluate the performance of your machine learning algorithms.

Basic understanding of modern neural networks and their applications in computer vision and natural language understanding