- Learn the difference between regression and classification.
- Train a linear regression model to predict values.
- Learn to predict states using Logistic Regression.
Intro to Machine Learning with PyTorch
Quick Facts
particular | details | |||||
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Collaborators
Amazon Web Services,
+1 more
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Learning efforts
10 Hours Per Week
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Course overview
The Intro to Machine Learning with PyTorch certification course is an online self-paced programme designed for candidates who have a keen interest to learn new ideas in the field of technology. The demand for Machine Learning engineers is higher than the supply. Thus, this course has been curated for those who want to strive and stay firm in the competitive market. This is an online self-paced program by Udacity.
Candidates will learn, understand, create and implement the most predictive algorithms in the real-time world. The candidate will deal with both supervised and unsupervised learning. They will have a deep understanding of Neural Networks. The course has a high demand because of its portable and dynamic nature.
Additionally, the 3-course projects engaged in this Intro to Machine Learning with PyTorch training will help the participant to develop a detailed and elaborated knowledge of this subject. It will serve as an opportunity to understand the importance of machine learning with PyTorch in detail.
The highlights
- Online self-paced learning
- 3 months course duration
- Projects based
- Real-world projects from industry experts
- 10 hours/week time investment
- Certification by Udacity
Program offerings
- Project feedback
- Real-world projects
- Project reviews
- Personal career coaching.
Course and certificate fees
The fee for Intro to Machine Learning with PyTorch is summarized as follows:
Particulars | Amount |
Annual Fee | Rs. 20,500 |
Monthly fee - pay as you go | Rs. 10,250/month |
certificate availability
Yes
certificate providing authority
Udacity
Who it is for
This course can be ideal for all those candidates who want themselves to be on their pathway to becoming ML Engineers or Python Programmers.
Eligibility criteria
Work Experience
Candidates willing to apply for the Intro to Machine Learning with the PyTorch programme should have a minimum programming experience of 40 hours.
He/she should have experience with libraries namely Pandas and NumPy.
Education
The candidate needs to have a basic knowledge of statistics and probability. He/She should possess knowledge on how to calculate variance and mean of a probability distribution. The candidate should be familiar with lists and dictionaries
Certification Qualifying Details
The estimated course duration is 3 months. During this duration, the candidate will learn about Machine learning with PyTorch. The syllabus includes three topics which are needed to be covered for the duration of 3 months. Each topic is combined with the one-course project for a better understanding of this subject. The candidate has to devote 10 hours per week in order to complete the program successfully. The Intro to Machine Learning with PyTorch certificate by Udacity will be awarded, thereafter.
What you will learn
After the completion of this Intro to Machine Learning with PyTorch certification syllabus, the candidate will learn the following-
- The core idea of machine learning, and Python
- Comparison between regression and classification
- Understand the concept of decision trees
- Create an image classifier on their own
- Learn to apply Bayes’ rule to predict cases of spam messages
- Build professional presentations
- Implement image classification application using deep neural network
- Learn to use PyTorch
- Understand the Gaussian mixture models and its use
- Learn to manage the access using the different tools
- Master the best practices for a real-time world
- Understand the basics of clustering
The syllabus
Course 1: Supervised Learning
Lesson 1: Regression
Lesson 2: Perceptron Algorithms
- Learn the definition of a perceptron as a building block for neural networks, and the perceptron algorithm for classification.
Lesson 3: Decision Trees
- Train decision trees to predict states.
- Use entropy to build decision trees, recursively
Lesson 4: Naive Bayes
- Learn Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm.
- Train models using Bayesian learning.
- Complete an exercise that uses Bayesian learning for natural language processing.
Lesson 5: Support Vector Machines
- Learn to train support vector machines to separate data, linearly.
- Use Kernel methods in order to train SVMs on data that is not linearly separable.
Lesson 6: Ensemble of Learners
- Build data visualizations for quantitative and categorical data.
- Create pie, bar, line, scatter, histogram, and boxplot charts.
- Build professional presentations.
Lesson 7: Evaluation Metrics
- Learn about different metrics to measure model success.
- Calculate accuracy, precision, and recall to measure the performance of your models.
Lesson 8: Training & Tuning Models
- Train and test models with Scikit-learn.
- Choose the best model using evaluation techniques like cross-validation and grid search.
Course 2: Introduction to Neural Networks with PyTorch
Lesson 1: Introduction to Neural Networks
- Learn the foundations of deep learning and neural networks.
- Implement gradient descent and backpropagation in Python.
Lesson 2: Implementing Gradient Descent
- Implement gradient descent using NumPy matrix multiplication.
Lesson 3: Training Neural Networks
- Learn several techniques to effectively train a neural network.
- Prevent overfitting of training data and learn best practices for minimizing the error of a network.
Lesson 4: Deep Learning with PyTorch
- Learn how to use PyTorch for building deep learning models.
Course 3: Unsupervised Learning
Lesson 1: Clustering
- Learn the basics of clustering data.
- Cluster data with the K-means algorithm
Lesson 2: Hierarchical & Density-Based Clustering
- Cluster data with single linkage clustering.
- Cluster data with DBSCAN, a clustering method that captures the insight that clusters are dense group of points.
Lesson 3: Gaussian Mixture Models
- Cluster data with Gaussian mixture models.
- Optimize Gaussian mixture models with and expectation maximization.
Lesson 4: Dimensionality Reduction
- Reduce the dimensionality of the data using principal.
- Component analysis and independent component analysis.
Admission details
Intro to Machine Learning with PyTorch admission requires the candidate to undergo the following steps-
Step 1- Visit the official website of the programme https://www.udacity.com/course/intro-to-machine-learning-nanodegree--nd229
Step 2- Click on Enroll Now button on the top of the page.
Step 3- You will be directed to a page wherein both payment options namely, Pay Upfront and Pay as you go is given.
Step 4: Choose the desired plan. Regular users will have ‘Regular Student’ button on their page while new users will have ‘Quick Checkout’ as option.
Step 5: Select Quick Checkout and then sign up with your Google or Facebook ID.
Step 6: Then a page will come wherein all the details of fee payment will be given. If you have coupon code then apply it and click on checkout or else directly click on continue with checkout.
Step 7: The payment will be made by the user with debit card, credit card, net banking and others.
Step 8: After doing the payment, a transaction slip will be shared. Then you will be able to check the course and access it.
Scholarship Details
Participants applying for Intro to Machine Learning with PyTorch classes can do so by visiting the link https://udacity.zendesk.com/hc/en-us/categories/360002443511-Scholarship-Programs. The candidate needs to then see whether he/she is eligible for scholarship. Hence, it is essential to sign up and submit the information after clicking ‘Notify Me’. Post this, the candidates will be regularly sent notifications for the scholarships posted on the website.
How it helps
The demand for machine learning engineers is far more than the supply. Health care sectors, finance to market predictions, and many other industries are changed by machine learning. A candidate with appropriate knowledge of the same will have great career advancements.
This Intro to Machine Learning with PyTorch certification benefits by helping the learner to know about the foundational machine learning skills which can be applied in the real world. The machine learning techniques taught in the course can be applied to various tasks such as image classification and customer segmentation. Several learning algorithms applications learnt in the duration of the course will enable the learner to demonstrate skills in a better manner. It will help enhance the practical skills needed to start.
Instructors
FAQs
Can a tablet be preferred over a computer for learning this program?
A tablet is not recommended as they have less computing power. However, OS X, Linux laptops or desktops and modern Windows will work well. The necessary instructions will be provided to install the required software packages in the same.
What applications and programming languages will be used for this course?
The applications and programming languages that will be used for this nanodegree program include Scikit-learn, NumPy, PyTorch, Python, Anacondas, Panda and Jupyter Notebook.
What will be the best software and version needed in this program?
A computer with a 64-bit operating system with 8GB of RAM will serve the purpose the best. It will also have an administrator account permission which should be enough to install programs including Anaconda with Python 3.x.
Are there any prerequisites for enrolling to this course on Intro to Machine Learning with PyTorch online course?
Basic knowledge of statistics and probability along with Intermediate Python programming knowledge is recommended before enrolling in this course. In case someone does not fulfill the requirements, a few other courses like Intro to Data Science will help one prepare for this program.
Is there any free trial session prior to payment for this program?
A seven-day free trial session has been planned. The candidate may scroll down the page to reach the fee details information. Start free trial option will be available. Click on it and proceed to enjoy your free trial sessions.
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