Machine Learning with TensorFlow on Google Cloud

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Start-Tech Academy

3:18:19

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1. Introduction

1. Introduction.mp4

05:13

2. Basics of Machine Learning

1. Linear regression basics.mp4

10:24

2. Logistic regression basics.mp4

11:40

3. Perceptron - Introduction to neural network

1. Single Neural Cell.mp4

02:50

2. Example of a Perceptron.mp4

07:00

3. What are Activation Functions.mp4

02:56

4. Sigmoid Activation Function.mp4

04:37

5. Linear regression case study.mp4

02:09

6. Linear regression case study - demonstration.mp4

18:02

7. Logistic regression case study.mp4

03:05

8. Logistic regression case study - demonstration.mp4

11:58

4. Artificial neural network

1. Parallel vs Sequential Stacking.mp4

06:32

2. Important terms.mp4

03:15

3. How Neural Networks work.mp4

04:03

4. Finding the optima using Gradient Descent.mp4

04:18

5. Concept Behind Using Gradient Descent.mp4

03:59

6. Types and Uses of Activation Functions.mp4

05:38

7. Multiclass Classification.mp4

03:21

8. Difference Between Gradient Descent and Stochastic Gradient Descent.mp4

02:10

9. Epochs.mp4

01:40

5. Creating arctificial neural network on Google Colab

1. Dataset for classification.mp4

09:45

2. Normalization and Test-Train split.mp4

06:11

3. Different ways to create ANN.mp4

02:09

4. Building the Neural Network.mp4

12:37

5. Compiling and Training the Neural Network model.mp4

09:53

6. Evaluating performance and Predicting.mp4

07:32

7. Building Neural Network for Regression Problem.mp4

21:43

8. Complex ANN Architectures using Functional API.mp4

13:39

9. Understanding Checkpoints and Callbacks in Keras.html

Description

Build, train, and deploy ML models with TensorFlow: A hands-on journey through Google Cloud's powerful infrastructure

What You'll Learn?

Master the foundational principles behind simple ML models such as Linear and Logistic Regression models using TensorFlow.
Construct intricate Artificial Neural Networks (ANN) to tackle more complex data challenges.
Design Convolutional Neural Networks (CNN) for image and pattern recognition tasks.
Harness the capabilities of Google Cloud's Colab to execute Python codes for ML tasks efficiently.
Explore the functionalities of Google Vertex and how it augments Jupyter notebook constructions.
Implement end-to-end machine learning workflows, from data preprocessing to model deployment

Who is this for?

Aspiring data enthusiasts keen on exploring machine learning using TensorFlow.

Developers looking to leverage cloud infrastructure for ML tasks.

Professionals eager to combine TensorFlow's capabilities with Google Cloud.

Beginners seeking a structured introduction to ML on the cloud.

Experienced learners aiming to deepen their knowledge and skillset in the field of ML using TensorFlow on GCP.

What You Need to Know?

Basic knowledge of Python and familiarity with Jupyter notebooks; beginners welcome, as foundational concepts are covered.

More details

Description
If you're a budding data enthusiast, developer, or even an experienced professional wanting to make the leap into the ever-growing world of machine learning, have you often wondered how to integrate the power of TensorFlow with the vast scalability of Google Cloud? Do you dream of deploying robust ML models seamlessly without the fuss of infrastructure management?
Delve deep into the realms of machine learning with our structured guide on "Machine Learning with TensorFlow on Google Cloud." This course isn't just about theory; it's a hands-on journey, uniquely tailored to help you utilize TensorFlow's prowess on the expansive infrastructure that Google Cloud offers.
In this course, you will:
Develop foundational models such as Linear and Logistic Regression using TensorFlow.
Master advanced architectures like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) for intricate tasks.
Harness the power and convenience of Google Cloud's Colab to run Python code effortlessly.
Construct sophisticated Jupyter notebooks with real-world datasets on Google Colab and Vertex.
But why dive into TensorFlow on Google Cloud? As machine learning solutions become increasingly critical in decision-making, predicting trends, and understanding vast datasets, TensorFlow's integration with Google Cloud is the key to rapid prototyping, scalable computations, and cost-effective solutions.
Throughout your learning journey, you'll immerse yourself in a series of projects and exercises, from constructing your very first ML model to deploying intricate deep learning networks on the cloud.
This course stands apart because it bridges the gap between theory and practical deployment, ensuring that once you've completed it, you're not just knowledgeable but are genuinely ready to apply these skills in real-world scenarios.
Take the next step in your machine learning adventure. Join us, and let's build, deploy, and scale together.
Who this course is for:
Aspiring data enthusiasts keen on exploring machine learning using TensorFlow.
Developers looking to leverage cloud infrastructure for ML tasks.
Professionals eager to combine TensorFlow's capabilities with Google Cloud.
Beginners seeking a structured introduction to ML on the cloud.
Experienced learners aiming to deepen their knowledge and skillset in the field of ML using TensorFlow on GCP.

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Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

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