Download Udemy – Learn Python for Data Science & Machine Learning from AZ 2021-10

• NumPy – a library that makes a variety of mathematical and statistical operations easier. It is also the basis for many features of the pandas library.

• Pandas – A Python library created specifically to facilitate working with data, it is the bread and butter of many Python data science tasks.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that creates charts just like you’d find in Excel or Google Sheets. Combining hands-on work with solid theoretical instruction, we’ll help you master the fundamentals of Python programming for data science. This Machine Learning with Python course covers the basics of machine learning using Python. You’ll learn about supervised learning versus unsupervised learning, look at how statistical modeling relates to machine learning, and compare each. We know that theory is important to build a solid foundation, we know that theory alone doesn’t do the job, so this course is full of practical examples that you can follow step by step. Even if you already have coding experience or want to learn about the advanced features of the Python programming language, this course is for you! Python coding experience is required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT professionals, database developers, and many others. Adding Python programming language skills to your resume will help you in any of these data specializations that require mastery of statistical techniques. Together, we’ll teach you the essentials you need to know not only about how to write code in Python, analyze and visualize data, and use machine learning algorithms, but also how to get paid for new programming skills. developed itself. This course includes 5 main areas:

1: Introduction to Python course for DS+ML

This introductory section gives you a thorough introduction to the Python for Data Science and Machine Learning course, the data science industry and market, job opportunities and salaries, and various data science job roles.

• An introduction to data science + machine learning with Python
• Data science industry and market
• Job opportunities in data science
• How to get a data science job
• Concepts and algorithms of machine learning

2: Python data analysis/visualization

This section gives you a complete introduction to data analysis and data visualization with Python with a step-by-step tutorial.

• Python crash course
• NumPy data analysis
• Pandas data analysis

3: Mathematics for data science

This section gives you a thorough introduction to the mathematics of data science such as statistics and probability.

• Descriptive Statistics
• Measuring variability
• Inferential statistics
• Possibility
• Hypothesis testing

4: Machine learning

This section gives you a thorough introduction to machine learning including supervised and unsupervised ML with hands-on step-by-step tutorials.

• An introduction to machine learning
• Data preprocessing
• Linear regression
• logistic regression
• K-nearest neighbors
• Decision trees
• Group training
• Support vector machines
• K-Means Clustering
• PCA

5: Starting a career in data science

This section gives you a complete introduction to starting a career as a data scientist with step-by-step hands-on training.

• Create a resume
• Create a cover letter
• Personal branding
• Freelancer + Freelance websites
• The importance of having a website
• networking

By the end of the course you’ll be a professional data scientist with Python, applying for jobs with confidence and feeling good knowing you have the skills and knowledge to back it up.

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