Learn, Innovate, and Create
With the Best Minds in AI
Your journey to becoming an AI expert starts now!
No more waiting! Dive into the world of artificial intelligence with our Machine Learning Specialization.
Discover a world of potential with us!
GET EDUCATED
>> Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
>> Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
>> Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
>> Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model
Machine Learning specialists are in high demand across a wide range of industries and company sizes.
From tech giants like Google and Microsoft to financial institutions, healthcare organizations, e-commerce platforms, social media companies, and the automotive industry, all are leveraging machine learning for various applications.
Small and medium-sized enterprises also hire these professionals to improve customer experiences, enhance operational efficiency, and support data-driven decision making. Furthermore, consulting firms and outsourcing agencies seek machine learning talent to assist their clients in implementing AI solutions.
So, whether in large corporations, startups, or SMEs, opportunities for machine learning professionals are vast and varied.
This Machine Learning Specialization is designed for learners of all levels and backgrounds who are interested in understanding machine learning, AI, and data science. While prior coding experience and a basic understanding of mathematics will be beneficial, the course is designed to be accessible to complete beginners as well.
The course is designed for:
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Aspiring Data Scientists: The comprehensive coverage of fundamental machine learning techniques makes it an ideal starting point.
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Software Engineers: For software developers looking to add machine learning to their toolkit, this course will provide hands-on experience in developing machine learning models, enabling them to bring new capabilities to their software projects.
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AI Enthusiasts: The teaching from leading AI expert Andrew Ng and the focus on practical applications makes it ideal for those wanting to dive deeper into AI.
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Students: Students studying computer science, data science, statistics, or related fields can greatly benefit from this course as a supplement to their academic studies. It provides practical, real-world experience and insights that can complement their academic learning.
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Career Changers: Those looking to transition into a new career in the tech industry, particularly in roles related to AI and machine learning, will find this course provides a solid foundation of knowledge and skills in these areas.
COURSE STRUCTURE
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning. AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
MODULE 1
Skills: Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Linear Regression, Logistic Regression for Classification
Supervised Machine Learning: Regression and Classification
This module, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code. You'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!
Learning outcome
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Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn
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Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression
MODULE 2
Skills: Artificial Neural Network, Xgboost, Tensorflow,
Advanced Learning Algorithms
This module, learn to utilize neural networks for classification tasks using TensorFlow. You'll get hands-on experience building a neural network in Python and learn about efficient parallel processing implementations. Deepen your understanding by studying model training in TensorFlow, exploring various activation functions, and extending your skills to multiclass classification. You'll learn about the Adam optimizer, its benefits for neural network training, and get introduced to different types of layers. Gain insight into best practices for tuning your model and improving your training data to boost algorithm performance. Finally, familiarize yourself with decision trees, a commonly used learning algorithm, along with its variations like random forests and boosted trees (XGBoost).
Learning outcome
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Build and train a neural network with TensorFlow to perform multi-class classification
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Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
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Build and use decision trees and tree ensemble methods, including random forests and boosted trees
MODULE 3
Skills: Collaborative Filtering, Unsupervised Learning, Recommender Systems,
Unsupervised Learning, Recommenders, Reinforcement Learning
In this module, you'll explore unsupervised learning algorithms, including clustering and anomaly detection. You'll gain an understanding of K-means algorithm, learn how to optimize it and use it for spotting unusual events. You'll also cover recommendation systems using both collaborative and content-based filtering with TensorFlow, learning about binary labels, mean normalization, and related items. Further, you'll discover reinforcement learning, creating a deep Q-learning neural network to land a virtual lunar lander on Mars. This includes working with state-action value functions, decision-making policies, and ε-greedy policy. Optional content delves deeper into feature reduction, PCA, and refinement strategies in reinforcement learning.
Learning outcome
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Build and train a neural network with TensorFlow to perform multi-class classification
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Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
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Build and use decision trees and tree ensemble methods, including random forests and boosted trees
By the end of this Specialization, you will be ready to:
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Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
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Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
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Build and train a neural network with TensorFlow to perform multi-class classification.
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Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
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Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
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Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
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Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
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Build a deep reinforcement learning model.
NICE TO MEET YOU
LEARN FROM EXPERIENCED DATA PROFESSIONALS
>> Andrew Ng, Stanford University, DeepLearning.AI
Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform. Dr. Ng now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.
>> Geoff Ladwig, DeepLearning.AI
Geoff Ladwig started as a Deep Learning student and a mentor for the Deep Learning Specialization. He worked as a consultant on the Natural Language Processing Specialization and as a Curriculum Engineer on the Machine Learning Specialization. Geoff has spent most of his career as an ASIC/Hardware/System engineer/architect in the communications and computer industries.
>> Eddy Shyu, product manager, DeepLearning.AI
Eddy Shyu has led the teams that built the Machine Learning Specialization, TensorFlow Advanced Techniques, as well as the Natural Language Processing Specialization, and AI for Medicine Specialization. Eddy was also co-instructor for Udacity's AI for Trading Nanodegree program.
>> Aarti Bagul, machine learning engineer, Snorkel AI
Before Snorkel, she worked closely with Andrew Ng in various capacities: She helped build and invest in machine learning companies at the AI Fund. Previously, she was a machine learning engineer at Landing AI and was the head teacher’s assistant for Dr. Ng’s deep learning class at Stanford University. She graduated with a Master's in Computer Science from Stanford and a Bachelor's in Computer Science and Computer Engineering from NYU with the highest honors.
"This course and in fact the whole specialization is a deep introduction to Machine Learning for beginners. It is detailed and yet a lot of fun because Andrew breaks down these difficult algorithms in to simple and intuitive parts and the practice labs are a great way to get introduced to the algorithms and how they are applied practically. In short this Machine Learning specialization is a definitely a great entry point for beginners."
- Amar K, July 2023
"This course is both informative and accessible, incorporating the latest machine learning techniques. It is extremely beginner-friendly and well worth recommending."
- Edward D, June 2023
"This final course of ML Specialization pretty solid. I can recommend this course to anyone who's trying to break in to AI. The instructor is the best. And the content is very well structured. Thank you so much DeepLearning.ai Team."
- Geethika I S, May 2023
Course
1350 €
including VAT
GET STARTED TODAY!
Machine Learning Specialization
(by AI visionary Andrew Ng)
Learning format
The total volume of training: 94 hours (126 academic hours) of independent work (including work on homework). Over 100 hours of practical tasks.
The course must be completed within two months.
Payment by invoice
Kindly provide your billing information in the comment field during registration. An invoice will be issued within 3 business days after registration for the training.
Before registering for the training, we kindly ask you to familiarize yourself with the curriculum of and terms of the training organization.
Ettevõtluskeskus OÜ is an authorized partner of Eesti Töötukassa, the Unemployment Office.
By registering for the course, you acknowledge and agree to the terms of use regarding the personal data provided to the Entrepreneurship Center (Ettevõtluskeskus OÜ).
Join millions of learners who have revolutionized their careers with highly rated MLS course.
Learn from the pioneers and become an expert in the field. The power of AI is just a click away!
HESITATING TO TAKE THE LEAP?
We genuinely understand the intricacies and challenges of self-paced learning. That's why our dedication to student success goes beyond mere content delivery:
>> Dynamic Progress Tracking: We closely monitor each student's journey, noting their progress, strengths, and areas of challenge. This allows us to proactively step in when necessary, ensuring that no student feels left behind.
>> Motivational Assistance: Learning can sometimes bring moments of doubt or frustration. In such instances, our team is right there to motivate and provide the push needed to overcome obstacles. Your success is our primary goal, and we're committed to ensuring you get there.
>> Always Accessible: Whether you have a course-related query, technical issue, or simply need guidance, our support team is a message away. We pride ourselves on being responsive and genuinely invested in resolving your concerns.
>> A Constant Companion in Your Learning Journey: The path of self-education can sometimes feel lonely. But with us, you'll always feel the presence of a supportive community. Every step of the way, we're here, ensuring you not only learn but thrive.
Remember, every milestone you achieve is celebrated by us as well. At Ettevõtluskeskus, you're never alone in your educational journey.
