Build on your existing knowledge with intermediate machine learning pipelines techniques and real-world applications.
Not typically required
Confident developer; infrastructure scripting
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IntermediateBut what is a neural network? | Deep learning chapter 1
IntermediateThe Essential Main Ideas of Neural Networks
IntermediateTransformers Explained - How transformers work
IntermediateAI, Machine Learning, Deep Learning and Generative AI Explained
IntermediateHow AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile
IntermediateTransformer Neural Networks - EXPLAINED! (Attention is all you need)
IntermediateLearn Machine Learning Like a GENIUS and Not Waste Time
IntermediateAll Machine Learning algorithms explained in 17 min
IntermediateWhat is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)
IntermediateNatural Language Processing: Crash Course AI #7
IntermediateHow To Learn Math for Machine Learning FAST (Even With Zero Math Background)
IntermediateIntroduction to Machine Learning: Supervised Learning
IntermediateRegression Models
IntermediateSupervised Learning: Classification
IntermediateSupervised Learning: Regression
IntermediateSupervised Machine Learning: Regression and Classification
BeginnerUnsupervised Text Classification for Marketing Analytics
IntermediateData Science and Machine Learning Essentials
IntermediateAI skills for Engineers: Supervised Machine Learning
IntermediateData Science: Machine Learning
IntermediateFollow these courses in order to complete the learning path. Click on any course to enroll.
But what is a neural network? | Deep learning chapter 1
The Essential Main Ideas of Neural Networks
Transformers Explained - How transformers work
Learn AI, Machine Learning, Deep Learning and Generative AI Explained
How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile
Transformer Neural Networks - EXPLAINED! (Attention is all you need)
Learn Machine Learning Like a GENIUS and Not Waste Time
All Machine Learning algorithms explained in 17 min
Learn What is YOLO algorithm? | Deep Learning Tutorial 31 (TensorFlow, Keras & Python)
Natural Language Processing: Crash Course AI 7
How To Learn Math for Machine Learning FAST (Even With Zero Math Background)
Explore supervised machine learning algorithms, prediction tasks, and model selection. Learn to improve performance using linear/logistic regression, KNN, decision trees, ensembling methods, and kernel techniques like SVM.
This course covers regression analysis, least squares, and inference using regression models. It also explores special cases of the regression model, such as ANOVA and ANCOVA, and delves into the analysis of residuals and variability.
A course that focuses specifically on classification techniques within supervised learning, offered by IBM.
A course that focuses specifically on regression techniques within supervised learning, offered by IBM.
This is the first course in the Machine Learning Specialization. It provides a broad introduction to modern machine learning, including supervised learning (linear regression, logistic regression, neural networks, and decision trees). You will build machine learning models in Python using popular machine learning libraries Num Py and Scikit-Learn.
This course focuses on topic modeling for marketing data. You will learn to apply topic modeling to various marketing use cases, evaluate and tune topic models, and use them to classify documents. The course covers both traditional and neural network approaches to topic modeling.
An introductory course that covers the data science process, including data acquisition, cleaning, and transformation, using tools like R and Python.
This edX course focuses on the fundamentals of supervised machine learning, including both classification and regression. You will learn to apply various algorithms to real-life problems using Python and Scikit-Learn. The curriculum covers classification techniques and important concepts for evaluating and tuning your models.
Part of the Data Science Professional Certificate, this course covers popular machine learning algorithms, principal component analysis, and regularization. You will build a movie recommendation system.
Explore related content to expand your skills beyond this learning path.
Enroll in this path to track your progress and stay motivated.