1

Этап 1

Machine Learning for Software Engineers

2

Этап 2

Image Recognition with Machine Learning

3

Этап 3

Natural Language Processing with Machine Learning

4

Этап 4

Applied Machine Learning: Deep Learning for Industry

5

Этап 5

Applied Machine Learning: Industry Case Study with TensorFlow

1

Этап 1

Machine Learning for Software Engineers

2

Этап 2

Image Recognition with Machine Learning

3

Этап 3

Natural Language Processing with Machine Learning

4

Этап 4

Applied Machine Learning: Deep Learning for Industry

5

Этап 5

Applied Machine Learning: Industry Case Study with TensorFlow

01 сентября 2020 19 ноября 2020
Цель просрочена на 1226 дней

Цель заброшена

Автор не отписывался в цели 3 года 6 месяцев 26 дней

Карьера и работа

Become a Machine Learning Engineer

If you're a developer looking to kick your career up a notch by adding Machine Learning to your skills, you’re in the right place.

Machine Learning skills are some of the most sought-after on the job market today, with ML Engineers making tens of thousands of dollars per year more than other developers. This track is focused on giving you the practical skills to solve real-world ML problems and applications, rather than emphasizing complex theory.

By the time you’re done, you’ll have the ability to get hired as a Machine Learning developer.

 Критерий завершения

finished all the courses

  1. Machine Learning for Software Engineers

    If you're a software engineer looking to add Machine Learning to your skillset, this is the place to start.

    This course will teach you to write useful code and create impactful Machine Learning applications immediately. From the start, you'll be given all the tools that you need to create industry-level machine learning projects. Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning. All topics are taught with industry standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

    Basic knowledge about Python is a prerequisite to this course.

    This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

    1. 1. What you'll learn from this course

    2. 2. Data Manipulation with NumPy

    3. 3. Data Analysis with pandas

    4. 4. Data Preprocessing with scikit-learn

    5. 5. Data Modeling with scikit-learn

    6. 6. Clustering with scikit-learn

    7. 7. Gradient Boosting with XGBoost

    8. 8. Deep Learning with TensorFlow

    9. 9. Deep Learning with Keras

  2. Image Recognition with Machine Learning

    If you want to dive into the technology behind computer vision and self driving cars, this is where to start.

    In this course you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. After completing this course, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks.

    The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites.

    This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

    1. 1. What you'll learn from this course

    2. 2. Image Processing

    3. 3. CNN

    4. 4. SqueezeNet

    5. 5. ResNet

  3. Natural Language Processing with Machine Learning

    In this course you'll learn techniques for processing text data, creating word embeddings, and using long short-term memory networks (LSTM) for tasks such as semantic analysis and machine translation. After completing this course, you will be able to solve the important day-to-day NLP problems faced in industry, which is incredibly useful given the prevalence of text data.

    The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites.

    This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

    1. 1. What you'll learn from this course

    2. 2. Word Embeddings

    3. 3. Language Model

    4. 4. Text Classification

    5. 5. Seq2Seq Model

  4. Applied Machine Learning: Deep Learning for Industry

    In this course, you'll level up your skills learned in the Industry Case Study and Machine Learning for Software Engineers. You'll take the modeling and data pipeline concepts and apply them to production-level classification and regression models for industry deployment, while continuing to practice the most efficient techniques for building scalable machine learning models. After this course, you will be able to complete industry-level machine learning projects, from data pipeline creation to model deployment and inference.

    The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites.

    This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

    1. 1. What you'll learn in this course

    2. 2. Data Pipeline

    3. 3. Model Execution

  5. Applied Machine Learning: Industry Case Study with TensorFlow

    In this course, you'll work on an industry-level machine learning project based on predicting weekly retail sales given different factors. You will learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this course, you will be able to take on industry-level machine learning projects, from data analysis to creating efficient models and providing results and insights.

    The code for this course is built around the TensorFlow framework, which is one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Basic knowledge of Python and TensorFlow are prerequisites. To get some experience with TensorFlow, try our course: Machine Learning for Software Engineers.

    This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

    1. 1. What you'll learn from this course

    2. 2. Preliminary Data Analysis

    3. 3. Data Processing

    4. 4. Model Predictions

  • 414
  • 01 сентября 2020, 07:55
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