Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
11 Hour(s) 3 Minute(s)
Language
English
Taught by
Full Stack Datascientist
Rating
Course Overview
Learn about machine learning, deep learning, text analytics using python and tensorflow
This course is for anyone who is interested in machine learning, deep learning and text analytics. This course assumes no previous knowledge, this course will also cover the basics of python and all the essential libraries(Pandas, Numpy, Matplotlib, Sklearn, TensorFlow, NLTK etc.) that will help students in their data science journey.
Course Content
- 7 section(s)
- 147 lecture(s)
- Section 1 Introduction
- Section 2 Python Basics
- Section 3 PandasNumpyMatplotlibEDA
- Section 4 Basics of Machine Learning
- Section 5 Machine Learning Algorithms
- Section 6 Text Analytics
- Section 7 Deep Learning
What You’ll Learn
- Learn about the basics of python as a language: basic syntax, data structure in python ,conditional statements and loops , expression and operators, Functions, Learn about essential Python libraries for data science/ analysis e.g. Pandas, Matplotlib, Numpy, Learn to do exploratory data analysis in Python, Learn how to deal with categorical variables, numerical variables, Learn about missing value analysis, outlier analysis, feature transformation etc., Learn about basics of machine learning : supervised/ unsupervised, regression/ classification, metrics used for regression and classification, Learn about popular machine learning algorithms (Keep an eye on this section, this will be updated as per demand), Learn how decision tree, association rule, naive bayes etc. works by building them from scratch in excel (don't worry if you are not familiar with excel, everything will be explained), Learn how to handle text data. Learn about NLTK : Tokenization, Lemmatization etc. Learn about regex. Learn about bag of words and TF-IDF approach. Build a text classification Model, Learn about the basics of TensorFlow, Learn about Artifical Neural Network, Convolutional Neural Network and Recurrent Neural Network and implement it on MNIST data set
Skills covered in this course
Reviews
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SSarthak Pradhan
till now the content and lecture is fabulus.
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SSobhan Banerjee
sound quality not good
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RRavipudi Devasree
yes it is matching but i use anacomda for this jupter notebook
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MMasoom Ali
python installation link is not available and behalf of that another link has not been provided. didn't explain why this library is imported and how it will work .
