Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.

**This course includes:**

- 8.5 hours on-demand video
- 2 articles
- Full lifetime access
- Access on mobile and TV
- Certificate of completion

**What you'll learn**

- Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, my course on Udemy will help you apply machine learning to your work.
- Learn Machine Learning with Hands-On Examples
- What is Machine Learning?
- Machine Learning Terminology
- Evaluation Metrics
- What are Classification vs Regression?
- Evaluating Performance-Classification Error Metrics
- Evaluating Performance-Regression Error Metrics
- Supervised Learning
- Cross Validation and Bias Variance Trade-Off
- Use matplotlib and seaborn for data visualizations
- Machine Learning with SciKit Learn
- Linear Regression Algorithm
- Logistic Regresion Algorithm
- K Nearest Neighbors Algorithm
- Decision Trees And Random Forest Algorithm
- Support Vector Machine Algorithm
- Unsupervised Learning
- K Means Clustering Algorithm
- Hierarchical Clustering Algorithm
- Principal Component Analysis (PCA)
- Recommender System Algorithm

**Description**

Hello there,

Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, my course on Udemy here to help you apply machine learning to your work.

Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course.

Do you know data science needs will create 11.5 million job openings by 2026?

Data Science Careers Are Shaping The Future

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

If you want to learn one of the employer’s most request skills?

If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you “Complete Machine Learning & Data Science with Python | A-Z” a straightforward course for Python Programming Language and Machine Learning.

In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.

We will open the door of the Data Science and Machine Learning a-z world and will move deeper. You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn.

Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.

This Machine Learning course is for everyone!

My "Machine Learning with Hands-On Examples in Data Science" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).

Why we use a Python programming language in Machine learning?

Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.

What you will learn?

In this course, we will start from the very beginning and go all the way to the end of "Machine Learning" with examples.

Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.

During the course you will learn the following topics:

- What is Machine Learning?
- More About Machine Learning
- Machine Learning Terminology
- Evaluation Metrics
- What is Classification vs Regression?
- Evaluating Performance-Classification Error Metrics
- Evaluating Performance-Regression Error Metrics
- Machine Learning with Python
- Supervised Learning
- Cross-Validation and Bias Variance Trade-Off
- Use Matplotlib and seaborn for data visualizations
- Machine Learning with SciKit Learn
- Linear Regression Theory
- Logistic Regression Theory
- Logistic Regression with Python
- K Nearest Neighbors Algorithm Theory
- K Nearest Neighbors Algorithm With Python
- K Nearest Neighbors Algorithm Project Overview
- K Nearest Neighbors Algorithm Project Solutions
- Decision Trees And Random Forest Algorithm Theory
- Decision Trees And Random Forest Algorithm With Python
- Decision Trees And Random Forest Algorithm Project Overview
- Decision Trees And Random Forest Algorithm Project Solutions
- Support Vector Machines Algorithm Theory
- Support Vector Machines Algorithm With Python
- Support Vector Machines Algorithm Project Overview
- Support Vector Machines Algorithm Project Solutions
- Unsupervised Learning Overview
- K Means Clustering Algorithm Theory
- K Means Clustering Algorithm With Python
- K Means Clustering Algorithm Project Overview
- K Means Clustering Algorithm Project Solutions
- Hierarchical Clustering Algorithm Theory
- Hierarchical Clustering Algorithm With Python
- Principal Component Analysis (PCA) Theory
- Principal Component Analysis (PCA) With Python
- Recommender System Algorithm Theory
- Recommender System Algorithm With Python

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

**Also See : Data Engineer/Data Scientist - Power BI/ Python/ ETL/SSIS**

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