Python & Tensorflow: Deep Dive Into Machine Learning

Python & TensorFlow: The Roadmap to Deep Machine Learning Expertise

What you'll learn

Grasp fundamentals of machine learning, deep learning, and their applications

Set up and navigate TensorFlow, understanding its architecture and APIs

Master supervised learning algorithms such as linear regression, SVMs, and decision trees

Dive into unsupervised techniques including clustering and PCA

Understand and construct neural networks, including CNNs and RNNs, using TensorFlow

Evaluate and optimize ML models, addressing overfitting and mastering hyperparameter tuning

Deploy TensorFlow models in production environments

Apply skills in a hands-on image classification project

Transition from Python basics to advanced ML & TensorFlow applications

Requirements

Basic Python Knowledge: Familiarity with Python's syntax and basic programming constructs

Foundational Math Skills: Understanding of algebra, and a basic grasp of calculus and statistics would be beneficial, especially for grasping underlying algorithms

Computer with Internet Access: To download required software, access course materials, and run Python and TensorFlow

Enthusiasm for Machine Learning: A keen interest to delve into the intricacies of ML and DL

Python Environment Setup: Having an environment like Jupyter Notebook or any IDE suitable for Python (e.g., PyCharm) could be advantageous

Basic Understanding of Data Structures: Familiarity with lists, arrays, matrices, etc., given the data-centric nature of the course

Logical & Analytical Thinking: Ability to approach problems methodically and think critically

Willingness to Experiment: Given the hands-on nature of ML and TensorFlow projects, being open to trying things out and learning from mistakes is crucial

Description

Welcome to our Python & TensorFlow for Machine Learning complete course. This intensive program is designed for both beginners eager to dive into the world of data science and seasoned professionals looking to deepen their understanding of machine learning, deep learning, and TensorFlow's capabilities.Starting with Python—a cornerstone of modern AI development—we'll guide you through its essential features and libraries that make data manipulation and analysis a breeze. As we delve into machine learning, you'll learn the foundational algorithms and techniques, moving seamlessly from supervised to unsupervised learning, paving the way for the magic of deep learning.With TensorFlow, one of the most dynamic and widely-used deep learning frameworks, we'll uncover how to craft sophisticated neural network architectures, optimize models, and deploy AI-powered solutions. We don't just want you to learn—we aim for you to master. By the course's end, you'll not only grasp the theories but also gain hands-on experience, ensuring that you're industry-ready.Whether you aspire to innovate in AI research or implement solutions in business settings, this comprehensive course promises a profound understanding, equipping you with the tools and knowledge to harness the power of Python, Machine Learning, and TensorFlow.We're excited about this journey, and we hope to see you inside!

Overview

Section 1: Introduction to Machine & Deep Learning

Lecture 1 What is Machine Learning?

Lecture 2 Types of Machine Learning

Lecture 3 Applications of Machine Learning

Lecture 4 What is Deep Learning?

Section 2: Basics of TensorFlow & Installation

Lecture 5 What is TensorFlow?

Lecture 6 Installing and Setting up TensorFlow

Lecture 7 TensorFlow Architecture

Lecture 8 A refresher on APIs

Lecture 9 TensorFlow APls

Section 3: Machine Learning Part 1 : Supervised Learning

Lecture 10 What is Supervised Learning?

Lecture 11 Linear Regression

Lecture 12 Logistic Regression

Lecture 13 Decision Trees

Lecture 14 Random Forests

Lecture 15 Support Vector Machines (SVMs)

Section 4: Machine Learning Part 2 : Unsupervised Learning

Lecture 16 What is Unsupervised Learning?

Lecture 17 K-Means Clustering

Lecture 18 Hierarchical Clustering

Lecture 19 Principal Component Analysis (PCA)

Section 5: Deep Learning Basics with Tensorflow : Neural Networks

Lecture 20 What are Neural Networks?

Lecture 21 Basic Neural Networks

Lecture 22 Convolutional Neural Networks (CNNs)

Lecture 23 Recurrent Neural Networks (RNNs)

Lecture 24 Building Deep Neural Networks

Section 6: Model Evaluation & Optimization

Lecture 25 Training and Testing Data

Lecture 26 Model Evaluation Metrics

Lecture 27 Overfitting and Underfitting

Lecture 28 Hyperparameter Tuning

Section 7: TensorFlow for Production

Lecture 29 Saving and restoring models

Lecture 30 Deploying TensorFlow models

Lecture 31 Distributed TensorFlow

Lecture 32 TensorBoard for visualization and debugging

Section 8: Project: Image Classification

Lecture 33 ML Project : Image classification Model

Section 9: Conclusion

Lecture 34 Conclusion

Beginners in Data Science and AI: Individuals looking to kick-start their journey in machine learning and deep learning,Python Developers: Programmers familiar with Python seeking to expand their skill set into AI and TensorFlow applications,Data Analysts and Statisticians: Professionals looking to transition or incorporate machine learning techniques into their analysis workflows,Tech Enthusiasts: Those curious about the latest trends in AI and wanting to get hands-on with TensorFlow and Python,Students: Undergraduates or postgraduates studying computer science, data science, or a related field and wanting a comprehensive and practical overview,Career Changers: Professionals from other fields wanting to pivot into data science or AI roles,Researchers: Individuals in scientific or academic roles looking to understand or employ ML techniques in their work,Business Professionals: Managers or decision-makers wanting to understand the capabilities and limitations of machine learning and how it can impact their business,Freelancers: Developers or consultants looking to expand their service offerings by mastering machine learning tools and frameworks