Data Science, Machine Learning and Deep Learning with Python
.MP4 | Video: 1280×720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 4.21 GB
Duration: 14.5 hours | Genre: eLearning | Language: English
Learn Data Science with Data Parsing, Data Visualization, Data Processing and Supervised Machine Learning, Unsupervised.
What you’ll learn
From beginner level to advanced level understanding of :
Data Science:(Online Data Parsing, Data visualization, Data Preprocessing, Preparing data for machine learning)
Machine Learning:(Supervised Machine Learning, Unsupervised Machine Learning, Implementation of algorithms form scratch, Built-in algorithms usages.)
amitDeep Learning:(Tensorflow, Hyperparameter tunings)
Working with some data sets which are benchmarks in industry like : Titanic, Seeds, Rock and Mine
Basic Knowledge of any programming language
Passion of learning
This course focuses on the fundamentals of Data Science, Machine learning and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning, there is no other choice then "python" as a programming language.
First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.
Then we moved on towards the Data Science and we start from data parsing using scrapy then the data visualizations by using several libraries of python and finally we end up by learning different data preprocessing techniques. And in the end, there is a complete project that we’ll do together.
After that, we’ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.
Finally, Deep learning will be discussed, the basic structure of an artificial neural network and it’s implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’ll improve the performance of the model.
Who this course is for:
Those who are interested in Artificial Intelligence
Those who have basic level of understanding of english
Those who have basic knowledge of any programming language
Those who have basic knowledge of OOP
hose who wants to write programs for predictions
Those who are interested in making automated computer programs
Those who wants to unlock the future of IT that is AI
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