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
Instructor: EDUCBA
Access provided by Ladoke Akintola University of Technology
Gain insight into a topic and learn the fundamentals.
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
Details to know
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Assessments
21 assignments
Taught in English
Recently updated!
September 2025
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There are 5 modules in this course
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.
What's included
9 videos4 assignments1 plugin
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.
What's included
14 videos4 assignments
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.
What's included
38 videos5 assignments
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.
What's included
22 videos4 assignments
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.
What's included
27 videos4 assignments
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