Introduction to Machine Learning Course by Udacity: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The Introduction to Machine Learning Course is a free programme of ten weeks. This particular course is a part of the Data Analyst Nanodegree. The candidate can opt for that course after the completion of this one. The Introduction to Machine Learning Course provided is of the intermediate level. It will teach the end-to-end procedure and functioning of investigating data with the help of the machine learning lens. The course curriculum is based on self-paced learning. The Introduction to Machine Learning Course syllabus contains ten modules in total. The candidate will be given an Introduction to Machine Learning Course at the end of the course completion. The entire course will be taught by the industrial experts, along with complete training for each concept.

Course and certificate fees

Type of course

Free

Introduction to Machine Learning Course fees

Particulars	Amount
Course fee	Nil

certificate availability

No

Eligibility criteria

Education

The candidate needs to have basic knowledge about Python and Statistics for applying to the Introduction to Machine Learning course. If a candidate didn’t have the basic knowledge about these aspects, then they have to first complete the introductory course among Intro to Python Programming, Inferential Statistics, and Descriptive Statistics, and then they can apply in the course.

What you will learn

Machine learning

By the end of the Introduction to Machine Learning Course programme, the candidate will learn the following concepts -

A few of the essential machine learning methods will be taught to students, as well as how to extract and select valuable characteristics that best reflect their data
They will comprehend the procedure for evaluating the performance of machine learning algorithms
They will practice applying learning in a mini-project in which they will eliminate residuals from a real dataset and reimplement their regressor
During the course, the technique for eliminating outliers to enhance the quality of linear regression predictions will be taught
They will learn to distinguish between unsupervised and supervised learning
Students will learn to implement the K-means with Python in programming
Enrolled candidates will make their own decision tree using Python
They would learn about the formulas for entropy in the middle of the course

The syllabus

Lesson 1: Welcome to the machine learning

Learn what machine learning is and meet Sebastian Thrun
Find out where machine learning is applied in technology and science

Lesson 2: Naive Bayes

Use Naive Bayes with scikit learn in python
Calculate the posterior probability and the prior probability of simple distributions
Splitting data between training sets and testing sets with scikit learn

Lesson 3: Support vector machines

Learn the simple intuition behind support vector machines
Identify how to choose the right kernel for your SVM and learn about RBF and linear kernels
Implement an SVM classifier in SKLearn/scikit-learn

Lesson 4: Decision trees

Code your own decision tree in python
Implement a mini-project where you identify the authors in a body of emails using a decision tree in Python
Learn the formulas for entropy and information gain and how to calculate them

Lesson 5: Choose your own algorithm

Decide how to pick the right machine learning algorithm among K-Means, Adaboost, and decision trees

Lesson 6: Datasets and questions

You'll be investigating one of the biggest frauds in American history
Apply your machine learning knowledge by looking for patterns in the Enron email dataset

Lesson 7: Regression

Code a linear regression in Python with scikit-learn
Understand how continuous supervised learning is different from discrete learning
Understand different error metrics such as SSE, and R Squared in the context of linear regressions

Lesson 8: Outliers

Remove outliers to improve the quality of your linear regression predictions
Apply your learning in a mini-project where you remove the residuals on a real dataset and reimplement your regressor
Apply your same understanding of outliers and residuals on the Enron Email Corpus

Lesson 9: Clustering

Identify the difference between unsupervised learning and supervised learning
Apply your knowledge of the Enron Finance Data to find clusters in a real dataset
Implement K-Means in Python and Scikit Learn to find the center of clusters

Lesson 10: Feature scaling

Use a min max scaler in sklearn
Understand how to preprocess data with feature scaling to improve your algorithms

Admission details

Follow the procedure to enroll in the Introduction to Machine Learning Course -

Step 1: For the detailed and complete course information, follow the below-mentioned link https://www.udacity.com/course/intro-to-machine-learning--ud120.

Step 2: Select the “start free course” option.

Step 3: Sign in with any social account, such as Google or Facebook. If the applicant is not already registered, he or she should first sign up at Udacity.

Step 4: After logging in, the candidate's classroom will be updated with the course.

Step 5: The enrolling process is complete.

Filling the form

As such, there is no application procedure in regards to this programme. Candidates just join in with any social account, and the curriculum will be added to their classroom.

Scholarship Details

In terms of the Introduction to Machine Learning Course, Udacity offers a variety of scholarships. There are 10 scholarship options available to students in total. Before applying for any scholarship, the candidate should review the qualifying requirements. Candidates can check the page https://www.udacity.com/scholarships to see whether they meet the qualifying requirements for the scholarship.

How it helps

During the course, the applicant will receive an Introduction to Machine Learning Course benefit as part of the curriculum, which industry professionals teach. The candidate will get career help and assistance during the course, and Udacity will keep them updated on available opportunities after the training is completed. Furthermore, the applicant will have Introduction to Machine Learning Course benefits to learn about the dynamics of the field, and they will study in line with that, making the candidate's job prospects brighter.

Instructors

Mr Sebastian Thrun
President
Udacity

FAQs

What is the total duration of the Introduction to Machine Learning course? Is it flexible?

The course duration is approx ten weeks. It needs to be completed within the given timeline.

Will the student get career assistance during the course?

No, during this course, no career assistance is provided. When the candidate is promoted to the nano degree programme, they will get complete career assistance.