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Become Machine Learning Engineer

Name: Become Machine Learning Engineer
Rating: 4.2 (34 reviews)

Master machine learning algorithms, data preprocessing, and real-world model deployment with Python and essential tools.

Created byPractical Machine Learning

Last updated 9/2024

English

English [Auto],

What you'll learn

Build and deploy end-to-end machine learning models in real-world applications.
Master key machine learning algorithms like regression, classification, and clustering.
Preprocess, clean, and analyze data to improve model performance.
Implement machine learning workflows using Python and essential libraries like Scikit-Learn and TensorFlow.

Course content

4 sections • 12 lectures • 1h 56m total length

Logistic Regression: From Zero to Hero11:00
Explore logistic regression as a non-linear classifier using the logistic curve to separate binary and multi-class data, with geometric and probabilistic interpretations via hyperplane distance and w^T x.
Demystifying Logistic Regression Math9:37
Explore logistic regression concepts, introducing the sigmoid function to map scores to probabilities, and apply regularization with lambda, L1/L2, and elastic net to control overfitting.
Logistic Regression: Real-World Examples10:18
Examine logistic regression through loss interpretations, including logistic loss and hinge loss, and connect them to models like SVM. Learn train-test splits, cross-validation, and data normalization to improve unseen-data performance.

Data Cleaning: The Unsung Hero of ML12:05
Clean and preprocess datasets for machine learning by imputing missing values, encoding features, and scaling numerical columns, then train logistic regression with scikit-learn and evaluate with F1 score and metrics.
Feature Engineering Magic: Transform Your Data8:42
Apply feature engineering and data preprocessing techniques—one-hot encoding, the knn imputer, and scaling—then train a logistic regression model with cross-validation to optimize auc and recall.
Know Your Model: Essential Evaluation Metrics7:39
Understand how to evaluate models using five-fold cross-validation, auc, recall, precision, and backward and forward feature selection to optimize logistic regression.

NLP for Beginners: Start with Logistic Regression8:16
Explore natural language processing basics by tokenizing text, converting words to numerical vectors, and comparing bag-of-words and tf-idf approaches, with discussions on word embeddings and semantic meaning.
Supercharge Your NLP with Advanced Techniques13:04
Compare bag-of-words and TF-IDF, explore one-hot encoding and stopword handling with scikit-learn, and examine word2vec and pretrained vectors like Google News and GloVe for semantic meaning.
Transfer Learning: The NLP Shortcut You Need7:15
Explore transfer learning and tf-idf weighted average word2vec, combining bag of words and tf-idf for enhanced nlp representations.

Taming COVID-19 Data: A Data Scientist's Guide10:38
Analyze covid-19 data with Python to compute death-by-confirmed-case percentages and visualize trends using bar plots. Compare state-level data, explore mean values, and identify top regions like Gujarat, Maharashtra, and Ladakh.
Unmasking COVID-19 Trends: Data-Driven Insights7:03
Visualize covid-19 trends using correlation matrices, heat maps, and scatter plots to link confirmed, death, and cured cases across district and state data; explore time series and gaussian patterns.
The Machine Learning Lifecycle: From Data to Deployment10:27
Explore the machine learning lifecycle from problem definition and data selection to preprocessing, model training, evaluation, and deployment, with emphasis on low latency, error minimization, and interpretability.

Requirements

Basic knowledge of Python programming is recommended, but not required.
No prior machine learning experience needed—everything will be taught from the ground up.
A computer with internet access and the ability to install Python software.

Description

Unlock the creative potential of artificial intelligence with "Master the Machine Muse: Build Generative AI with ML." This comprehensive course takes you on an exciting journey into the world of generative AI, blending the art of machine learning with the science of creativity. Whether you're an aspiring data scientist, a tech enthusiast, or a creative professional looking to harness the power of AI, this course will provide you with the skills and knowledge to build and deploy your generative models.

Course Highlights:

- Introduction to Generative AI: Understand the fundamentals of generative AI and its applications across various domains such as art, music, text, and design.

- Foundations of Machine Learning: Learn the core concepts of machine learning, including supervised and unsupervised learning, and how they apply to generative models.

- Deep Learning for Creativity: Dive deep into neural networks and explore architectures like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformers that are driving the generative AI revolution.

- Hands-On Projects: Engage in practical, hands-on projects that will guide you through the process of building your generative models. From generating art to composing music, you'll experience the thrill of creating with AI.

- Python Programming: Gain proficiency in Python programming, focusing on libraries and frameworks essential for generative AI, such as TensorFlow, PyTorch, and Keras.

- Ethics and Future of Generative AI: Discuss the ethical considerations and future implications of generative AI, ensuring you are well-equipped to navigate this rapidly evolving field responsibly.

Who Should Enroll:

- Data Scientists and Machine Learning Engineers looking to specialize in generative models.

- Artists, Musicians, and Designers interested in exploring AI as a tool for creativity.

- Tech Enthusiasts and Innovators eager to stay ahead in the field of AI.

- Students and Professionals aiming to enhance their skill set with cutting-edge technology.

Prerequisites:

- Basic understanding of Python programming.

- Familiarity with machine learning concepts is beneficial but not required.

Course Outcomes:

By the end of this course, you will:

- Have a strong grasp of generative AI concepts and techniques.

- Be able to build and train generative models using state-of-the-art machine learning frameworks.

- Understand the ethical considerations and potential impacts of generative AI.

- Be prepared to apply generative AI skills in real-world projects and innovative applications.

Join us in "Master the Machine Muse: Build Generative AI with ML" and embark on a creative journey that merges technology with imagination, empowering you to shape the future of AI-driven creativity.

Who this course is for:

Aspiring machine learning engineers and data scientists.
Developers looking to transition into AI and machine learning roles.
Python programmers interested in enhancing their skill set with machine learning.
Students and professionals wanting to build machine learning models and solve real-world problems.

Become Machine Learning Engineer

What you'll learn

Explore related topics

Course content

Logistic Regression Fundamentals3 lectures • 31min

Data Preparation and Evaluation3 lectures • 28min

Logistic Regression for NLP3 lectures • 29min

Logistic Regression in Action: COVID-19 Case Study3 lectures • 28min

Requirements

Description

Who this course is for: