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# Machine Learning Course - A Beginner's Guide

#### 1: Machine Learning Course - A Beginner's Guide

Machine Learning Course - Simplified

Udemy Coupon Code

#### In this age of machine learning, every aspiring data scientist is expected to up-skill themselves in machine learning techniques & tools and apply them in real-world business problems.

Machine Learning problems can be divided into 3 broad classes:

• Supervised Machine Learning

• Unsupervised Machine Learning

• Reinforcement Learning

Supervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future – you would use Supervised Machine Learning algorithms. Supervised Machine Learning problems can again be divided into 2 kinds of problems:

• Classification Problems: When you want to classify outcomes into different classes. For example – whether a customer would default on their loan or not is a classification problem which is of high interest to any Bank

• Regression Problem: When you are interested in answering how much – these problems would fall under the Regression umbrella. For example – what is the expected amount of default from a customer is a Regression problem

• Unsupervised Machine Learning: There are times when you don’t want to exactly predict an Outcome. You just want to perform a segmentation or clustering. For example – a bank would want to have a segmentation of its customers to understand their behavior. This is an Unsupervised Machine Learning problem as we are not predicting any outcomes here.

• Reinforcement Learning: It is said to be the hope of true artificial intelligence. And it is rightly said so because the potential that Reinforcement Learning possesses is immense. It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future.

What you'll learn

• Understanding the basics of supervised and unsupervised learning
• Python libraries like Numpy, Pandas, etc. to analyze your data efficiently
• Linear Regression, Logistic Regression, and Decision Trees for building machine learning models
• Understand how to solve Classification and Regression problems using machine learning
• How to evaluate your machine learning models using the right evaluation metrics?
• Improve and enhance your machine learning model’s accuracy through feature engineering
• Projects covered - a) Customer Churn Prediction and b) NYC Taxi Trip Duration Prediction

Requirements

• This course requires no prior knowledge about Data Science or any tool.

Who this course is for:

• Beginners in Data Science

Machine Learning Steps
1. Collecting Data: As you know, machines initially learn from the data that you give them. ...
2. Preparing the Data: After you have your data, you have to prepare it. ...
3. Choosing a Model: ...
4. Training the Model: ...
5. Evaluating the Model: ...
6. Parameter Tuning: ...
7. Making Predictions.

Yes! There are thousands of online learning resources—like Gentle Introduction to Machine Learning—that are designed specifically for freshers and beginners. Even if you have no coding experience, you can start small and work your way up to algorithms and their implementation.

9 Best Machine Learning Courses [2023]
• Machine Learning Crash Course with TensorFlow APIs.
• Machine Learning for Data Science and Analytics by ColumbiaX.
• Machine Learning by HarvardX.
• Machine Learning A-Z: Hands-On Python and R In Data Science (Udemy)
• Machine Learning with Python by IBM.
• Machine Learning by Georgia Tech.

There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

So, should I learn machine learning or artificial intelligence first? If you're looking to get into fields such as natural language processing, computer vision or AI-related robotics then it would be best for you to learn AI first.

Traditional Machine Learning requires students to know software programming, which enables them to write machine learning algorithms. But in this groundbreaking Udemy course, you'll learn Machine Learning without any coding whatsoever. As a result, it's much easier and faster to learn!

Programming is a part of machine learning, but machine learning is much larger than just programming. In this post you will learn that you do not have to be a programmer to get started in machine learning or find solutions to complex problems

Yes. You don't have to be a PRO at math or Statistics but of course you have to know the concepts behind the Machine Learning algorithms, when to use them, why to use them and what hyper-parameter tunings will yield best results or predictions through the model you made!

Never fear, you're not late to this, and you're not behind. Even as AI has become pervasive in our individual lives, it's not yet widespread in product organizations.

INSTRUCTOR
Analytics Vidhya

Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.