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Data Science for Beginners: Hands-On Data Science in Python

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Data Science for Beginners: Hands-On Data Science in Python
Data Science for Beginners: Hands-On Data Science in Python, Learn Statistics, Python, Data pre-processing, Regression Analysis, Classification Techniques, Clustering, NLP, and More

  • HOT & NEW
  • Created by Vijay Gadhave
  •  English [Auto-generated]

PREVIEW THIS COURSE - GET COUPON CODE

What you'll learn

  • The Complete Understanding of Machine Learning from the Scratch
  • Learn Python for Data Science and Machine Learning
  • Learn How to Pre-Process the Data
  • Perform Linear and Logistic Regressions in Python
  • Learn Different Regression Algorithms in Python
  • Learn to Apply Different Classification Algorithms in Python
  • K means and Hierarchical Cluster Analysis
  • Data Analysis with NumPy and Pandas
  • Data Visualization with Matplotlib library
  • DataFrames, Pandas Series, Pandas Matrix
  • NumPy Arrays, Indexing, Selection, Numpy Operations
  • Learn to Work with Missing Data
  • Natural Language Processing

Description
Data Science, Machine Learning and Artificial Intelligence are the most demanding skills in today's world,

Almost every Multi-National company is working on these new technologies



With this Mega Course you will learn all the required tools for Data Science from very beginning !



We will cover below topics,

1) Data Analysis with Numpy: NumPy Arrays, Indexing and Selection, NumPy Operations

2) Data Analysis with Pandas: Pandas Series, DataFrames, Multi-index and index hierarchy, Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames, Pandas Operations, Reading and Writing Files

3) Data Visualization with Matplotlib library

4) Data Pre-Processing: Importing Libraries, Importing Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set, Feature scaling

5) Regression Analysis: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Decision Tree, Random Forest, Evaluating the Model Performance

6) Classification Techniques: Logistic Regression, KNN, SVM, Naïve Bayes, Decision Tree, Random Forest

7) Cluster Analysis: K means, Hierarchical

8) Natural Language Processing: NLTK, Tokenization, Stemming, Lemmatization, Stop Words, POS Tagging, Chunking, Named Entity Recognition, Text Classification



Learn Data Science to advance your Career and Increase your knowledge in a fun and practical way !



Regards,

Vijay Gadhave

Who this course is for:

  • Students who want to start a career in the field of Data Scientist and Machine Learning
  • Professionals who want to start a new career in Data Science
  • Anyone who is interested in Machine Learning and Data science
  • Data Analyst who want to level up in Machine Learning and Data Science

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