this is a list of all the certifications in ML

1. CERTIFICATIONS IN
MACHINE LEARNING

2. IBM Machine Learning Professional Certificate
SYLLABUS:
1. EXPLORATORY DATA ANALYSIS
2. SUPERVISED MACHINE LEARNING (REGRESSION)
3. SUPERVISED MACHINE LEARNING (CLASSIFICATION)
4. UNSUPERVISED MACHINE LEARNING
5. DEEP LEARNING AND REINFORCEMENT LEARNING
6. CAPSTONE PROJECT

3. https://www.coursera.org/professional-certificates/ibm-machine-learning
What you'll learn
Master the most up-to-date practical skills and knowledge
machine learning experts use in their daily roles
Learn how to compare and contrast different machine
learning algorithms by creating recommender systems in
Python
Develop working knowledge of KNN, PCA, and non-negative
matrix collaborative filtering
Predict course ratings by training a neural network and
constructing regression and classification models

4. COURSE OFFERED BY:
IBM

5. TENSORFLOW CERTIFICATION
https://www.tensorflow.org/certificate
SYLLABUS
1. ARTIFICIAL NEURAL NETWORKS
2. RECURRENT NEURAL NETWORKS
3. CONVOLUTIONAL NEURAL NETWORKS
4. IMAGE AUGMENTATION
5. TRANSFER LEARNING
6. MULTI CLASS CLASSIFICATION
7. COMPUTER VISION IN JAVASCRIPT
8. NATURAL LANGUAGE PROCESSING

6. TensorFlow Developer Certificate program overview
Exam | $100 USD
The goal of this certificate is to provide everyone in the world the
opportunity to showcase their expertise in ML in an increasingly AI-driven
global job market. This certificate in TensorFlow development is intended
as a foundational certificate for students, developers, and data scientists
who want to demonstrate practical machine learning skills through the
building and training of models using TensorFlow.

7. In order to successfully take the exam, test takers should be comfortable
with:
● Foundational principles of ML and Deep Learning
● Building ML models in TensorFlow 2.x
● Building image recognition, object detection, text recognition algorithms
with deep neural networks and convolutional neural networks
● Using real-world images in different shapes and sizes to visualize the
journey of an image through convolutions to understand how a computer
“sees” information, plot loss and accuracy
● Exploring strategies to prevent overfitting, including augmentation and
dropouts
● Applying neural networks to solve natural language processing problems
using TensorFlow

8. https://www.tensorflow.org/resources/learn-ml
The above link contains all the materials and course contents
that are needed to crack the exam. The entire course content is
divided into the following parts:
1. Coding skills
2. Mathematical skills
3. Machine learning theory and fundamentals
4. Project execution
The above website also contains the necessary books and colab
files for self learning and preparation along with online courses
sufficient for an individual to be ready for the exam.

9. GOOGLE MACHINE LEARNING CERTIFICATE
https://www.coursera.org/professional-certificates/preparing-for-
google-cloud-machine-learning-engineer-professional-certificate
SYLLABUS
1. INTRODUCTION TO AI AND ML IN GOOGLE CLOUD
2. LAUNCHING INTO MACHINE LEARNING
3. TENSORFLOW ON GOOGLE CLOUD
4. FEATURE ENGINEERING
5. MACHINE LEARNING IN ENTERPRISE
6. PRODUCTION MACHINE LEARNING SYSTEMS
7. COMPUTER VISION FUNDAMENTALS WITH GOOGLE CLOUD
8. NATURAL LANGUAGE PROCESSING
9. RECOMMENDATION SYSTEMS
10. MLOPS
11. ML PIPELINES
12. VERTEX AI DEPLOYMENT

10. What you'll learn
Learn the skills needed to be successful in a machine learning
engineering role
Prepare for the Google Cloud Professional Machine Learning
Engineer certification exam
Understand how to design, build, productionalize ML models to
solve business challenges using Google Cloud technologies
Understand the purpose of the Professional Machine Learning
Engineer certification and its relationship to other Google Cloud
certifications
Instructor: Google Cloud Training