Machine Learning With Python

Introduction to Machine Learning(ML)

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

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

Working With Pandas

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

Python Regular Expressions

  • What are regular expressions?
  • The match Function
  • The search Function
  • Matching vs searching
  • Search and Replace
  • Extended Regular Expressions
  • Wildcard

Python Oracle Database Access

  • Install the cx_Oracle and other Packages
  • Create Database Connection
  • CREATE, INSERT, READ, UPDATE and DELETE Operation
  • DML and DDL Operation with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

Regression Based Learning

  • Simple regression
  • Multiple regression
  • Logistic regression

Data Mining

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

Introducing Matplotlib

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

Clustering Based Learning

  • Defnition
  • Types of clustering
  • The k-means clustering algorithm
  • Predicting house prices with regression

Working With OpenCV

  • Setting up opencv
  • Loading and displaying images
  • Applying image filters
  • Tracking faces
  • Face recognition
  • Module 10:Performing predictions with Linear Regression
  • Simple linear regression
  • Multiple regression
  • Training and testing model

Introduction To Python

Installation of Python framework and packages: Anaconda & pip Writing/ Running python programs using Spyder Command Prompt Working with Jupyter, notebooks, Creating Python variables Numeric , string and logical operations Data containers : Lists , Dictionaries, Tuples & sets Practice assignment

Iterative Operations & Functions In Python

  • Writing for loops in Python
  • While loops and conditional blocks List/Dictionary comprehensions with loops.
  • Writing your own functions in Python Writing your own classes and functions.

Data Handling In Python Using NumPy & Pandas

  • Introduction to NumPy arrays, functions & properties Introduction to Pandas & data frames
  • Importing and exporting external data in Python Feature engineering using Python

Data Handling In Python Using NumPy & Pandas

  • Introduction to NumPy arrays, functions & properties Introduction to Pandas & data frames
  • Importing and exporting external data in Python Feature engineering using Python

Data Science & Machine Learning in Python

Machine Learning Basics

  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilities Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error

Generalised Linear Models In Python

  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models

Tree Models Using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation Introduction to bagging algorithm Random Forests
  • Grid search and randomized grid search ExtraTrees (Extremely Randomised Trees) Partial dependence plots

Support Vector Machines (SVM) & KNN In Python

Introduction to idea of observation based learning Distances and similarities k Nearest Neighbours (kNN) for classi cation Brief mathematical background on SVM/li> Regression with kNN & SVM

Unsupervised Learning In Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA) Di erence between PCAs and Latent
  • Factors Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering

Text Mining In Python

  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis