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Complete DataScience with Python and Tensorflow

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Complete DataScience with Python and Tensorflow, available at $49.99, has an average rating of 3.35, with 147 lectures, based on 60 reviews, and has 341 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who is interested to learn about machine learning, deep learning and text analytics It is particularly useful for Anyone who is interested to learn about machine learning, deep learning and text analytics.

Enroll now: Complete DataScience with Python and Tensorflow

Summary

Title: Complete DataScience with Python and Tensorflow

Price: $49.99

Average Rating: 3.35

Number of Lectures: 147

Number of Published Lectures: 147

Number of Curriculum Items: 147

Number of Published Curriculum Objects: 147

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • Anyone who is interested to learn about machine learning, deep learning and text analytics

Target Audiences

  • Anyone who is interested to learn about machine learning, deep learning and text analytics

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 Curriculum

Lecture 1: Introduction

Lecture 2: 2.Install Python

Lecture 3: 3.Installing Package

Chapter 1: Python Basics

Lecture 1: 1.Pythonbasics Introduction

Lecture 2: 2.Pythonbasics Lab

Lecture 3: 3.Pythonbasics Lab

Lecture 4: 4.Pythonbasics Lab

Lecture 5: 5.PythonBasics_Lab

Lecture 6: 6.Pythonbasics Lab

Lecture 7: 7.PythonBasics_Lab

Lecture 8: 8.Pythonbasics Lab

Lecture 9: 9.Pythonbasics Lab

Lecture 10: 10.Pythonbasics Lab

Lecture 11: 11.Pythonbasics Lab

Lecture 12: 12.Pythonbasics Lab

Lecture 13: 13.Pythonbasics Lab

Lecture 14: 14.Pythonbasics Lab

Lecture 15: 15.Pythonbasics Lab

Chapter 2: PandasNumpyMatplotlibEDA

Lecture 1: 1.Introduction_to_pandas_part1.mp4

Lecture 2: 2.Introduction_to_pandas_part2.mp4

Lecture 3: 3.Introduction To Pandas Part3

Lecture 4: 4.Introduction To Pandas Part4

Lecture 5: 5.Introduction To Pandas Part5

Lecture 6: 6.Introduction To Pandas Part6

Lecture 7: 7.Introduction To Pandas Part7

Lecture 8: 8.Introduction To Pandas Part8

Lecture 9: 9.Introduction To Pandas Part9

Lecture 10: 10.Introduction To Numpy Part1

Lecture 11: 11.Introduction To Numpy Part2

Lecture 12: 12.EDA_part1

Lecture 13: 13.Eda Part2

Lecture 14: 14.Eda Part3

Lecture 15: 15.Eda Part4

Lecture 16: 16.Eda Part5

Chapter 3: Basics of Machine Learning

Lecture 1: 1.MachineLearning_Basics_part1

Lecture 2: 2.MachineLearning_Basics_part2

Lecture 3: 3.MachineLearning_Basics_part3

Lecture 4: 4.Machinelearning_Basics_part4

Lecture 5: 5.Machinelearning_Basics_Part5

Lecture 6: 6.MachineLearning_Basics__Lab_Part1

Lecture 7: 7.Machinelearning_Basics__Lab_Part2

Lecture 8: 8.Machinelearning_Basics__Lab_Part3

Lecture 9: 9.Machinelearning_Basics__Lab_Part4

Lecture 10: 10.MachineLearning_Basics__Lab_Part5

Lecture 11: 11.Machinelearning_Basics__Lab_Part6

Lecture 12: 12.Machinelearning_Basics__Lab_Part7

Lecture 13: 13.Machinelearning_Basics__Lab_Part8

Lecture 14: 14.MachineLearning_Basics__Lab_Part9

Lecture 15: 15.Machinelearning_Basics__Lab_Part10

Lecture 16: 16.Machinelearning_Basics__Lab_Part11

Lecture 17: 17.Machinelearning_Basics__Lab_Part12

Lecture 18: 18.Machinelearning_Basics__Lab_Part13

Chapter 4: Machine Learning Algorithms

Lecture 1: 1.MLAlgo_Introduction

Lecture 2: 2.MLalgo Regression Part1

Lecture 3: 3.MLalgo Regression Part2

Lecture 4: 4.MLalgo Regression Part3

Lecture 5: 5.MLalgo Regression Lab Part1

Lecture 6: 6.MLalgo Regression Lab Part2

Lecture 7: 7.MLalgo Regression Lab Part3

Lecture 8: 8.MLalgo Regression Lab Part4

Lecture 9: 9.MLalgo Regression Lab Part5

Lecture 10: 10.MLalgo Regression Lab Part6

Lecture 11: 11.MLAlgo_Classification_Introduction

Lecture 12: 12.Mlalgo Classification Logisticregression

Lecture 13: 13.Mlalgo Classification Logisticregression

Lecture 14: 14.Mlalgo Decisiontrees Part1

Lecture 15: 15.Mlalgo Ensemblemodels

Lecture 16: 16.Mlalgo Naivebayes

Lecture 17: 17.Mlalgo Pca

Lecture 18: 18.MLalgo KNN

Lecture 19: 19.MLalgo Lda Qda

Lecture 20: 20.MLalgo Clustering

Lecture 21: 21.MLalgo AR

Lecture 22: 22.MLalgo Lab Part1

Lecture 23: 23.MLalgo Lab Part2

Lecture 24: 24.MLalgo Lab Part3

Lecture 25: 25.MLalgo Lab Part4

Lecture 26: 26.MLalgo Lab Part5

Lecture 27: 27.MLalgo Lab Part6

Lecture 28: 28.Mlalgo Lab Part7

Lecture 29: 29.Mlalgo Lab Part8

Lecture 30: 30.Mlalgo Lab Part9

Lecture 31: 31.Mlalgo Lab Part10

Lecture 32: 32.Mlalgo Lab Part11

Lecture 33: 33.Mlalgo Lab Part12

Lecture 34: 34.Mlalgo Kfold Cv

Lecture 35: 35.AssociationRules

Lecture 36: 36.K Mean Clustering

Lecture 37: 37.Naive Bayes Part1

Lecture 38: 38.Naive Bayes Part2

Lecture 39: 39.Ar Implementation

Lecture 40: 40.Decisiontree Part1

Lecture 41: 41.Decisiontree Part2

Lecture 42: 42.Decisiontree Part3

Chapter 5: Text Analytics

Lecture 1: 1.Text_Analytics_Regex_Part1

Instructors

Rating Distribution

  • 1 stars: 7 votes
  • 2 stars: 6 votes
  • 3 stars: 10 votes
  • 4 stars: 16 votes
  • 5 stars: 21 votes

Frequently Asked Questions


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