Published 04/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 78 lectures (10h 11m) | Size: 5 GB
Practical Implementation of algorithms in Machine Learning and Deep Learning using Test Cases in Python
What you’ll learn
Complete TensorFlow course for Google Certification
Programming using TensorFLow
Machine Learning models implementation using TensorFLow
Deep Learning Models implementation using TensorFLow
Natural Language model building using TensorFLow
Long Short Term Memory implementation using TensorFLow
Bidirectional-Long Short Term Memory implementation using TensorFLow
Gated Recurrent Units using TensorFLow
Convolution Neural Network using TensorFLow
Transfer Learning concepts
Requirements
Basic Programming knowledge of Python
Basic of Machine Learning
A mathematical and Programming course in Machine Learning by Rituraj Dixit
Description
This course is specially designed for those learners who want to build their career as a TensorFlow developer. The syllabus covered in this course will prepare learners for Google Certificate examination if they wish to take that. We have covered every minute detail and all important topics of TensorFlow along with the appropriate example which will help in solving the programming problems and clearing the doubts. The course is designed in such a way that learner will not feel tough in learning this important framework.
Our Course has more than 10 sections which goes from basic concepts to advance concepts like we start from simple tensor to ragged tensor to more advance concepts like Gradient Tape, or we go from sequential model building to functional model building using this frame work.
Broadly our course is designed keeping the syllabus of Google Certification for TensorFLow in mind which make this course stands apart from other peer courses.
Building a strong Neural Network model whether for classification or for regression is always a promising task and we know that Machine learning rely on this two pillars but their are two big challenges which are always their in building these models the first one is Overfitting and Underfitting, so in this course we will also make you introduce to two important tools Dropout and Callback and how to use them in avoiding overfitting.
Following Topics are broadly covered in our courses
Basics of TensorFLow
Sequential API
Functional API
Deep Learning Frame work
Convolution Neural Network
Basic Natural Language
Advance Natural language
Sequence Modelling
LSTM
Bi-LSTM
GRU Cells
Who this course is for
Python Developers how to want to earn TensorFlow Certification and learn the frame work for building ML Frame works
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