By the end of this course, learners will be able to build, train, and evaluate machine learning and deep learning models using Python, Scikit-learn, and TensorFlow. They will confidently preprocess datasets, apply classical algorithms, visualize insights, and design neural networks to solve real-world problems.
Master Machine Learning with TensorFlow: Basics to Advanced
Master Machine Learning with TensorFlow: Basics to Advanced
位教师：EDUCBA
访问权限由 New York State Department of Labor 提供
您将学到什么
Preprocess datasets, apply classical ML algorithms, and visualize insights in Python.
Build, train, and evaluate machine learning models with Scikit-learn.
Design and implement neural networks with TensorFlow for real-world problems.
您将获得的技能
- Feature Engineering
- Scikit Learn (Machine Learning Library)
- Machine Learning
- Matplotlib
- Seaborn
- Regression Analysis
- Data Preprocessing
- Development Environment
- Pandas (Python Package)
- Python Programming
- Convolutional Neural Networks
- NumPy
- Artificial Neural Networks
- Data Manipulation
- Tensorflow
- Jupyter
- Deep Learning
- 技能部分已折叠。显示 9 项技能，共 17 项。
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作业
21 项作业
授课语言：英语（English）
最近已更新！
September 2025
了解顶级公司的员工如何掌握热门技能
该课程共有5个模块
This module introduces learners to the foundations of machine learning, its real-world applications, and the tools needed to begin hands-on practice. Students explore what machine learning is, how machines learn, and where ML is applied across industries, setting the stage for practical TensorFlow projects.
涵盖的内容
9个视频4个作业
This module equips learners with essential ML tools such as Anaconda, Jupyter Notebook, and Python libraries. Students learn to manage environments, leverage third-party packages, and perform numerical computations with NumPy for efficient machine learning pipelines.
涵盖的内容
14个视频4个作业
This module focuses on preparing, analyzing, and visualizing data using Pandas, Matplotlib, and Seaborn. Learners handle complex datasets, manage missing values, and create insightful visualizations to uncover patterns, trends, and anomalies essential for ML readiness.
涵盖的内容
38个视频5个作业
This module covers essential preprocessing techniques, data transformation, and classical ML algorithms. Students practice feature engineering, scaling, encoding, and regression modeling while leveraging Scikit-learn to prepare clean and structured datasets.
涵盖的内容
22个视频4个作业
This module introduces deep learning with TensorFlow, covering computational graphs, operations, regression models, and neural networks. Students build and train models using activation functions, optimizers, and the MNIST dataset for hands-on image classification.
涵盖的内容
27个视频4个作业
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