INTRODUCTION

As per the TIOBE Index for May 2020, R is among the top 10 programming languages.

Machine Learning Using Python: According to the kdnuggets and businessoverbroadway survey, Python tops the chart with 83% of the respondents reported using Python as a regular use language and as per the TIOBE survey of May 2020 it is the 3rd most popular language. Python was created by a Dutch Programmer, Guido van Rossum in 1991. Python is an open source language which is also platform agnostic. One can easily work with data in various formats such as csv, tsv, json, xml, html etc. Python has large number of libraries suited for different requirements. With the help of packages such as numpy and pandas, one can unleash insights from data. For data modeling and machine learning, there are packages such as Scipy and Scikit-learn. The visualization capabilities of Matplotlib and Seaborn are of top notch quality. There are no prerequisites (no programming background expected) for the course. The course is self contained and comprehensive in all aspects of programming. The course covers Python, statistics, SQL and advanced machine learning topics. This is a live instructor-led program where the participants can ask questions and clear their doubts instantly.

UNIT 1: INTRODUCTION TO PYTHON

Machine Learning Using Python: kdnuggets businessoverbroadway
  • Python Shell and IDLE, Jupyter Overview
  • Variables , Identifiers ,Statements
  • Relational ,Logical and Arithmetic Operators, Precedence
  • Native Data Types-Strings,List,Tuples,Set,Dictionaries etc
  • List Slicing ,List slicing with Steps
  • Strings ,String Methods
  • Functions
  • Conditionals , Loops ,Nested Loops ,Functions
  • File I/O
  • Namespaces and Scope
  • Recursion, Error
  • Data Visualization basics
  • Importing data into Jupyter Notebook
  •  

UNIT 2: STATISTICS FOUNDATION

Machine Learning Using Python: kdnuggets businessoverbroadway
  • Measures of Central Tendency
  • Measures of Variation-Dispersion, Skewness ,Kurtosis
  • Random Variables
  • Probability , Bayes Theorem
  • Probability distribution-Discrete and Continuous
  • Sample Distribution, Sampling Distribution
  • Central Limit Theorem
  • Confidence Interval Estimation, Confidence Level
  • Hypothesis Testing
  • Z-Test ,T-Test ,F-Test ,ANOVA ,Chi square Test
  • Type 1 Error ,Type 2 Error
  •  

UNIT 3: SQL BASICS FOR DATA SCIENCE

Machine Learning Using Python: kdnuggets businessoverbroadway

UNIT 4: MACHINE LEARNING USING PYTHON

Machine Learning Using Python: kdnuggets businessoverbroadway
  • Data wrangling
  • Exploratory Data analysis and advanced data visualization-using different libraries:
    • Scientific Libraries- Pandas, Numpy, Scipy
Machine Learning Using Python: kdnuggets businessoverbroadway
  • Visualization Libraries- Matplotlib, Seaborn
Machine Learning Using Python: kdnuggets businessoverbroadway
  • Machine learning libraries– scikit Learn, Stats Model
Machine-learning-libraries-–scikit-Learn-Stats-Model
  • Machine Learning Overview:Supervised,Unsupervised,Reinforcement Learning
  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Support vector machines
  • Decision Trees-Classification and Regression Trees(CART)
  • Random Forest
  • K Nearest Neighbors
  • Clustering – K means clustering
  • Text Analytics-Sentiment Analytics
  • Time Series-AR,MA,ARMA,ARIMA
  • Dimension Reduction Techniques-Factor Analysis/Principal Component Analysis
  • Evaluating and improving the model, Key Performance metrics
Machine Learning Using Python: kdnuggets businessoverbroadway
Machine Learning Using Python: kdnuggets businessoverbroadway
Box-plot Machine Learning Using Python: kdnuggets businessoverbroadway
Correlation-plot Machine Learning Using Python: kdnuggets businessoverbroadway

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