Machine Learning with TensorFlow
Course Overview
The Machine Learning with TensorFlow course by CourseDeal is designed to teach learners how to build intelligent systems and predictive models using one of the most popular machine learning frameworks, TensorFlow. You’ll start with the fundamentals of machine learning, including data preprocessing, model building, and evaluation. The course emphasizes hands-on experience, guiding you through creating neural networks, deep learning architectures, and real-world AI solutions. You will also learn to work with large datasets, optimize model performance, and deploy models for practical applications. By the end of this course, learners will have a solid understanding of TensorFlow, neural networks, and machine learning workflows, enabling them to solve complex problems in business, healthcare, finance, and more.
Key Highlights
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Learn machine learning and deep learning concepts using TensorFlow
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Build neural networks and implement deep learning models
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Hands-on projects using real-world datasets
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Learn data preprocessing, model evaluation, and optimization
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Deploy ML models for real-world applications
Tools & Technologies Covered
- TensorFlow
- Keras
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- Google Colab
Curriculum
- 5 Sections
- 0 Lessons
- 11 Weeks
- Module 1: Introduction to Machine Learning and TensorFlowThis module introduces machine learning concepts, types of learning (supervised, unsupervised, and reinforcement learning), and real-world applications. You’ll learn the basics of TensorFlow, its architecture, and how to set up the environment. The module covers tensors, operations, and computational graphs, providing a strong foundation to develop ML models. Learners will also understand the importance of data preparation and feature engineering to ensure accurate and efficient learning.0
- Module 2: Data Preprocessing and Feature EngineeringIn this module, you’ll learn techniques to clean, transform, and normalize datasets to improve model performance. Topics include handling missing data, encoding categorical variables, scaling, and splitting data for training and testing. You’ll also learn feature selection and extraction methods to enhance predictive power. Hands-on exercises allow learners to apply preprocessing steps in TensorFlow pipelines and prepare datasets for model training.0
- Module 3: Building Neural Networks with TensorFlowThis module covers the construction of neural networks using TensorFlow and Keras. You’ll learn about layers, activation functions, optimizers, and loss functions. The module walks you through building feedforward neural networks, backpropagation, and gradient descent. Through practical exercises, you’ll train and evaluate models to solve classification and regression problems. Learners also explore techniques to prevent overfitting and underfitting for reliable predictions.0
- Module 4: Deep Learning ArchitecturesExplore advanced deep learning architectures such as Convolutional Neural Networks (CNNs) for image data, Recurrent Neural Networks (RNNs) for sequence data, and Long Short-Term Memory (LSTM) networks. This module demonstrates how to implement these models using TensorFlow, train them on real-world datasets, and evaluate their performance. Learners gain practical experience in building AI solutions for image recognition, natural language processing, and time-series forecasting.0
- Module 5: Model Optimization and EvaluationThis module focuses on improving model performance using techniques such as learning rate adjustments, regularization, dropout, and hyperparameter tuning. You’ll learn to evaluate models using accuracy, precision, recall, F1-score, and ROC curves. The module also covers cross-validation and ensemble learning to enhance prediction robustness. By completing this module, learners will be able to build high-performing, reliable machine learning models.0
Pass Percentage
