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Master Machine Learning with TensorFlow: Basics to Advanced

EDUCBA via Coursera

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Overview

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Build a strong foundation in machine learning, deep learning, and TensorFlow through a structured, hands-on learning experience that takes you from core concepts to practical model development. In this course, you will learn how machine learning works, explore real-world applications across industries, and set up a professional Python development environment using Jupyter Notebook, Anaconda, and essential data science libraries. As you progress, you will develop practical skills in data wrangling with Pandas, numerical computing with NumPy, and data visualization using Matplotlib and Seaborn. You will also learn how to preprocess datasets, engineer features, and build classical machine learning models with Scikit-learn before advancing to deep learning with TensorFlow. Through hands-on exercises and real-world datasets, you will train, optimize, and evaluate regression models and neural networks, including image classification with the MNIST dataset. Designed for beginners entering machine learning as well as professionals looking to strengthen their TensorFlow knowledge, this course combines clear explanations with coding practice, case studies, and assessments that reinforce every stage of the machine learning workflow. By the end of the course, you will be able to confidently preprocess data, build and evaluate machine learning and deep learning models, visualize insights, and apply industry-standard tools to solve real-world problems.

Syllabus

Getting Started with Machine Learning

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.

Tools of the Trade – Jupyter, Anaconda & Libraries

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.

Data Analysis & Visualization

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.

Preprocessing & Classical Machine Learning

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.

Deep Learning with TensorFlow

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.