MACHINE LEARNING TRAINING

MODULE 1: PYTHON ESSENTIALS

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

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    

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

• Definition
• 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