Python Course For Data Science and Machine Learning From A-Z

Python For Data Science and Machine Learning From A-Z course will help you learn Python for Data Science & Machine Learning from end-to-end.

You will learn how to program using Python for Data Science and Machine Learning in this useful, hands-on course. This includes how to analyze and visualize data as well as how to use it in a useful way.

Our major goal is to provide you with the education needed to become a professional Data Scientist with Python, obtain your first job, and understand the ins and outs of Python programming for Data Science and Machine Learning.

Python For Data Science and Machine Learning From A-Z Overview

In this course, we'll discuss some of the top and most crucial Python data science packages, including NumPy, Pandas, and Matplotlib.

• Many mathematical and statistical processes are made simpler by the NumPy library, which also serves as the foundation for many pandas library features.

• Pandas are the mainstay of a lot of Python data science work. It is a Python package designed expressly to deal with data easier.

For examining and experimenting with data, NumPy and Pandas are fantastic tools. A data visualization package called Matplotlib creates graphs similar to those in Google Sheets or Excel. We take you from the fundamentals of Python Programming for Data Science to expertise by fusing practical work with sound theoretical teaching.

The fundamentals of machine learning using Python are covered in this course. You'll study the differences between supervised and unsupervised learning, consider the connection between statistical modeling and machine learning, and compare the two.

We know that theory is necessary to lay a strong foundation, but theory by itself won't cut it, which is why this course is jam-packed with real-world, actionable examples that you can follow step by step. This course is for you whether you are new to programming or want to learn more about the sophisticated capabilities of the Python programming language.

Jobs for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and many more include Python coding skills as either a requirement or recommendation. You will gain a more competitive edge in any of these data specialties demanding knowledge of statistical methods if you add Python coding language abilities to your CV.

Together, we'll provide you with the fundamental knowledge you need to understand not just how to use machine learning algorithms, analyze and visualize data, and write Python code, but also how to get paid for your newly acquired programming talents.

The course covers 5 main areas:

1: Python for Data Science + Machine Learning Course Intro

You will learn everything about the Python for Data Science and Machine Learning course, the data science market and industry, job openings and wages, and the numerous data science job roles in this introductory section.

• Intro to Data Science + Machine Learning with Python

• Data Science Industry and Marketplace

• Data Science Job Opportunities

• How To Get a Data Science Job

• Machine Learning Concepts & Algorithms

2: Python Data Analysis/Visualization

You will receive a thorough introduction to data analysis and data visualization using Python in this section, along with practical, step-by-step training.

• Python Crash Course

• NumPy Data Analysis

• Pandas Data Analysis

3: Mathematics for Data Science

You will receive a thorough introduction to the mathematics used in data science, including statistics and probability, in this section.

• Descriptive Statistics

• Measure of Variability

• Inferential Statistics

• Probability

• Hypothesis Testing

4:  Machine Learning

You will receive a thorough introduction to machine learning in this section, including instruction on both supervised and unsupervised ML techniques.

• Intro to Machine Learning

• Data Preprocessing

• Linear Regression

• Logistic Regression

• K-Nearest Neighbors

• Decision Trees

• Ensemble Learning

• Support Vector Machines

• K-Means Clustering

• PCA

5: Starting A Data Science Career

In-depth information about how to begin a career as a data scientist with practical, step-by-step training is provided in this part.

• Creating a Resume

• Creating a Cover Letter

• Personal Branding

• Freelancing + Freelance websites

• Importance of Having a Website

• Networking

By the end of the course, you'll be an experienced Python Data Scientist who can confidently apply for jobs and feel good about it because you have the qualifications to support it.

Who this course is for:

• Students who want to learn about Python for Data Science and Machine Learning