PYTHON + DATA SCIENCE + MACHINE LEARNING

Python is mainly stated as high-level, general-purpose programming language, which emphasizes code readability. The syntax helps the programmers to express their concepts in few general "lines of code" when compared with other promising languages, like Java or C++. Through our courses, you can easily construct clear programs, on both large and small scales.As the importance of Python programming language is gaining huge popularity, therefore; the need to understand and know the language is increasing among people. When you think about Python training, you must look for an Ducat expert help.

MODULE 1: PYTHON ESSENTIALS

  • What is Python..?
  • A Brief history of Python
  • Why Should I learn Python..?
  • Installing Python
  • How to execute Python program
  • Write your first program
  • VARIABLES & DATA TYPES
  • Variables
  • Numbers
  • String
  • Lists ,Tuples & Dictionary

CONDITIONAL STATEMENTS & LOOPS

  • if...statement
  • if...else statement
  • elif...statement
  • The while...Loop
  • The for....Loop

CONTROL STATEMENTS

  • continue statement
  • break statement
  • pass statement

FUNCTIONS

  • Define function
  • Calling a function
  • Function arguments
  • Built-in functions

MODULES & PACKAGES

  • Modules
  • How to import a module...?
  • Packages
  • How to create packages

CLASSES & OBJECTS

  • Introduction about classes & objects
  • Creating a class & object
  • Inheritance
  • Methods Overriding
  • Data hiding

FILES & EXCEPTION HANDLING

  • Writing data to a file
  • Reading data from a file
  • Read and Write data from csv file
  • try...except
  • try...except...else
  • finally
  • os module

DATA SCIENCE

MODULE 1 : GETTING STARTED WITH PYTHON LIBRARIES

  • what is data analysis ?
  • why python for data analysis ?
  • Essential Python Libraries
  • Installation and setup
  • Ipython
  • Jupyter Notebook

MODULE 2 : NUMPY ARRAYS

  • Creating multidimensional array
  • NumPy-Data types
  • Array attributes
  • Indexing and Slicing
  • Creating array views and copies
  • Manipulating array shapes
  • I/O with NumPy

MODULE 3 : WORKING WITH PANDAS

  • Installing pandas
  • Pandas dataframes
  • Pandas Series
  • Data aggregation with Pandas DataFrames
  • Concatenating and appending DataFrames
  • Joining DataFrames
  • Handling missing data

MODULE 4 : DATA LOADING,STORAGE AND FILE FORMAT

  • Writing CSV files with numpy and pandas
  • HDF5 format
  • Reading and Writing to Excel with pandas
  • JSON data
  • Parsing HTML with Beautiful Soup
  • PyTables

MODULE 5 : STATISTICS AND LINEAR ALGEBRA

  • Basic statistics with numpy
  • Linear Algebra with numpy
  • Numpy random numbers
  • Creating a numpy masked array

MODULE 6 : DATA VISUALIZATION

  • Installation matplotlib
  • Basic matplotlib plots
  • Scatter plots
  • Saving plots to file
  • plotting functions in pandas

MODULE 7 : INTRODUCTION TO MACHINE LEARNING

  • What is ML..?
  • Types of ML
  • Decision trees
  • Linear regression
  • Logistic regression
  • Naive Bayes
  • k-Nearest Neighbors

MODULE 8: NATURAL LANGUAGE PROCESSING

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words with NLTK
  • Stemming words with NLTK
  • Speech tagging
  • Sentiment analysis with NLTK

MODULE 9: INTRODUCTION TO OPENCV

  • Setting up opencv
  • Loading and displaying images
  • Applying image filters
  • Tracking faces
  • Face recognition

MODULE 10: WORKING WITH BIG DATA

  • What is Hadoop?
  • MapReduce
  • File handling with Hadoopy
  • Pig
  • Pyspark

MACHINE LEARNING

INTRODUCTION TO MACHINE LEARNING

  • What is Machine learing?
  • Overview about sci-kit learn and tensorflow
  • Types of ML
  • Some complementing fields of ML
  • ML algorithms
  • Machine learning examples

MODULE 2: REGRESSION BASED LEARNING

  • Simple regression
  • Multiple regression
  • Logistic regression
  • Predicting house prices with regression

MODULE 3: CLUSTERING BASED LEARNING

  • Defnition
  • Types of clustering
  • The k-means clustering algorithm

MODULE 4: DATA MINING

  • Introducing data mining
  • Decision Tree
  • Affity Analysis
  • Clustering

MODULE 5: CLASSIFIATION – SENTIMENT ANALYSIS

MODULE 6: NATURAL LANGUAGE PROCESSING

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words with NLTK
  • Stemming words with NLTK
  • Speech tagging
  • Sentiment analysis with NLTK

MODULE 7: MAKING SENSE OF DATA THROUGH VISUALIZATION

  • Introducing matplotlib
  • Bar Charts
  • Line Charts
  • Scatter plots
  • Bubble charts

MODULE 8: WORKING WITH OPENCV

  • Setting up opencv
  • Loading and displaying images
  • Applying image filters
  • Tracking faces
  • Face recognition

MODULE 9: PERFORMING PREDICTIONS WITH LINEAR REGRESSION

  • Simple linear regression
  • Multiple regression
  • Training and testing model

MODULE 10: SUPPORT VECTOR MACHINES(SVM)

MODULE 11: NEURAL NETWORKS

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