Python & TensorFlow: Deep Dive into Machine Learning

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Meta Brains

3:01:18

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1. Introduction to Machine & Deep Learning

1. What is Machine Learning.mp4

02:41

2. Types of Machine Learning.mp4

03:11

3. Applications of Machine Learning.mp4

02:55

4. What is Deep Learning.mp4

04:09

2. Basics of TensorFlow & Installation

1. What is TensorFlow.mp4

04:30

2. Installing and Setting up TensorFlow.mp4

03:14

3. TensorFlow Architecture.mp4

04:23

4. A refresher on APIs.mp4

08:00

5. TensorFlow APls.mp4

03:37

3. Machine Learning Part 1 Supervised Learning

1. What is Supervised Learning.mp4

02:55

2. Linear Regression.mp4

09:52

3. Logistic Regression.mp4

13:05

4. Decision Trees.mp4

07:41

5. Random Forests.mp4

08:05

6. Support Vector Machines (SVMs).mp4

05:15

4. Machine Learning Part 2 Unsupervised Learning

1. What is Unsupervised Learning.mp4

09:07

2. K-Means Clustering.mp4

05:56

3. Hierarchical Clustering.mp4

06:29

4. Principal Component Analysis (PCA).mp4

03:48

5. Deep Learning Basics with Tensorflow Neural Networks

1. What are Neural Networks.mp4

03:39

2. Basic Neural Networks.mp4

05:06

3. Convolutional Neural Networks (CNNs).mp4

05:38

4. Recurrent Neural Networks (RNNs).mp4

04:00

5. Building Deep Neural Networks.mp4

04:37

6. Model Evaluation & Optimization

1. Training and Testing Data.mp4

03:55

2. Model Evaluation Metrics.mp4

05:02

3. Overfitting and Underfitting.mp4

07:11

4. Hyperparameter Tuning.mp4

04:20

7. TensorFlow for Production

1. Saving and restoring models.mp4

04:09

2. Deploying TensorFlow models.mp4

04:06

3. Distributed TensorFlow.mp4

04:29

4. TensorBoard for visualization and debugging.mp4

05:58

8. Project Image Classification

1. ML Project Image classification Model.mp4

05:26

9. Conclusion

1. Conclusion.mp4

04:49

Description

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

Who is this for?

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

What You Need to Know?

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

More details

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 Pythonâ€”a cornerstone of modern AI developmentâ€”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 learnâ€”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!

Who this course is for:
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

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