Machine Learning Using Python

Basic Python

1  Introduction

1.1    What is Python..?

1.2   A Brief history of Python

1.3   Installing Python

1.4   How to execute Python program

2. Using the Python Interpreter
   • 1. Invoking the Interpreter
     • 1.1. Argument Passing
     • 1.2. Interactive Mode
   • 2. The Interpreter and Its Environment
     • 2.1. Source Code Encoding

3. An Informal Introduction to Python
   • 1. Using Python as a Calculator
     • 1.1. Numbers
     • 1.2. Strings
     • 1.3. Lists
   • 2. First Steps Towards Programming

4. More Control Flow Tools
   • 1. if Statements
   • 2. for Statements
   • 3. The range() Function
   • 4. break and continue Statements, and else Clauses on Loops
   • 5. pass Statements
   • 6. Defining Functions
   • 7. More on Defining Functions
     • 7.1. Default Argument Values
     • 7.2. Keyword Arguments
     • 7.3. Arbitrary Argument Lists
     • 7.4. Unpacking Argument Lists
     • 7.5. Lambda Expressions
     • 7.6. Documentation Strings
     • 7.7. Function Annotations
   • 8. Intermezzo: Coding Style

5. Data Structures
   • 1. More on Lists
     • 1.1. Using Lists as Stacks
     • 1.2. Using Lists as Queues
     • 1.3. List Comprehensions
     • 1.4. Nested List Comprehensions
   • 2. The del statement
   • 3. Tuples and Sequences
   • 4. Sets
   • 5. Dictionaries
   • 6. Looping Techniques
   • 7. More on Conditions
   • 8. Comparing Sequences and Other Types

6. Modules
   • 1. More on Modules
     • 1.1. Executing modules as scripts
     • 1.2. The Module Search Path
     • 1.3. “Compiled” Python files
   • 2. Standard Modules
   • 3. The dir() Function
   • 4. Packages
     • 4.1. Importing * From a Package
     • 4.2. Intra-package References
     • 4.3. Packages in Multiple Directories

7. Input and Output
   • 1. Fancier Output Formatting
     • 1.1. Formatted String Literals
     • 1.2. The String format() Method
     • 1.3. Manual String Formatting
     • 1.4. Old string formatting
   • 2. Reading and Writing Files
     • 2.1. Methods of File Objects
     • 2.2. Saving structured data with json

Data Science with Python

1. Install Anaconda Distribution as per OS from https://www.anaconda.com/distribution/ (Python 3.7 version)
2. Sign Up for account creation on https://www.hackerrank.com/
3. Sign up for account creation on https://www.kaggle.com/
4. Sign up for account creation on https://github.com/
5. Git Bash Utility – https://git-scm.com/downloads

Module 1: Statistics and Probability

1. Descriptive Statistics:

• Central tendency: Mean, Median, Mode
• Sample variance
• Standard deviation
• Random Variables: Discrete, Continuous
• Probability density functions
• Binomial distribution
• Expected Value, E(X)
• Poisson Process
• Law of large numbers
• Standard normal distribution and empirical rule
• Z-score

1. Inferential Statistics:

• Central limit theorem
• Sampling distribution of the sample mean
• Standard error of the mean
• Mean and variance of Bernoulli distribution
• Margin of error 1
• Margin of error 2
• Confidence interval
• Hypothesis testing and p-value
• One-tailed and two tailed tests
• Z-statistics and T-statistics
• Type 1 error
• Squared error of regression line
• Co-efficient of determination
• Chi-square distribution
• Pearson’s chi square test (goodness of fit)
• Co-relation and casualty.

Module 2: Data Analysis using Python

1. Numpy

• Numpy Vector and Matrix
• Functions – arange(), zeros(), ones(), linspace(), eye(),
• reshape(), random(), max(), min(),
• argmax(), argmin(), shape and dtype attribute
• Indexing and Selection
• Numpy Operations – Array with Array, Array with Scalars,
• Universal Array Functions

1. Pandas

• Pandas Series
• Pandas Data-Frame
• Missing Data (Imputation)
• Group by Operations
• Merging, Joining and Concatenating Data-Frame.
• Pandas Operations
• Data Input and Output from wide variety of formats like csv, excel, db and html etc.

Module 3: Data Visualization using python Matplotlib, Seaborn, Pandas-in built, Plotly and Cufflinks

1. Matplotlib

• plot() using Functional approach
• multi-plot using subplot()
• figure() using OO API Methods
• add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
• Customization – figure size, impoving dpi, Plot appearance,
• Markers, Control over axis appearance and special Plot Types

1. Seaborn

• Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(), kdeplot()
• Categorical Plots using barplot(), countplot(), boxplot(), violinplot(), stripplot(), swarmplot(), factorplot()
• Matrix Plots using heatmap(), clustermap()
• Grid Plots using PairGrid(), FacetGrid()
• Regression Plots using lmplot()
• Styles and Colors customization.

1. Plotly and Cufflinks

• Interactive Plotting using Plotly and Cufflinks

1. Pandas Built-in

• Histogram, Area Plot, Bar Plot, Scatter Plot, Box-plot, Hex-plot, Kde-plot, Density Plot e. Choropleth Maps
• Interactive World Map and US Map using Plotly and Cufflinks Module

Module 4: GIT

• Distribution Version Control System
• How internally, GIT Manages Version Control on Changesets.
• Creating Repository
• Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git cat-file, git pull, git push, git commit
• Managing Configuration – System Level, User Level, Repository level

Module 5: Jupyter Notebook

• Introduction, Basic Commands, Keyboard Shortcut and Magic Functions

Module 6: Linear Algebra and Calculus

• Vector and Matrix, basic operations
• Trigonometry
• Derivatives

Module 7: SQL

• MySQL Server and Client Installation
• SQL Queries
• CRUD Operations
• Types of tables(Fact and dimension)

Module 8: Big Data

• What is big data?
• What is distributed computing?
• What is parallel processing?
• Why data scientist require big data?

Module 9: Machine Learning Introduction

• What is Machine Learning?
• Machine Learning Process Flow-Diagram
• Different Categories of Machine Leaning – Supervised, Unsupervised and Reinforcement
• Scikit-Learn Overview
• Scikit-Learn cheat-sheet

Module 10: Regression

• Linear Regression
• Robust Regression (RANSAC Algorithm)
• Exploratory Data Analysis (EDA)
• Correlation Analysis and Feature Selection
• Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Co-efficient of
• Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
• Polynomial Regression
• Regularized Regression – Ridge, Lasso and Elastic Net Regression
• Bias-Variance Trade-Off
• Cross Validation – Hold Out and K-Fold Cross Validation
• Data Pre-Processing – Standardization, Min-Max, Normalization and Binarization
• Gradient Descent

Module 11: Classification – Logistic Regression

• Sigmoid function
• Logistic Regression learning using Stochastic Gradient Descent (SGD)
• SGDClassifier
• Measuring accuracy using Cross-Validation, Stratified k-fold
• Confusion Matrix – True Positive (TP), False Positive (FP), False
• Negative (FN), True Negative (TN)
• Precision, Recall, F1 Score, Precision/Recall Trade-Off
• Receiver Operating Characteristics (ROC) Curve.

Module 12: Classification – k-Nearest Neighbor(KNN)

• Classification and Regression
• Application, Advantages and Disadvantages
• Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski
• Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score.

Module 13: Classification – SVM (Support Vector Machine)

• Classification and Regression
• Separating line, Margin and Support Vectors
• Linear SVC Classification
• Polynomial Kernel – Kernel Trick
• Gaussian Radial Basis Function (rbf)
• Grid Search to tune hyper-parameters.
• Support Vector Regression.

Module 14: Classification –Decision Trees

• CART (Classification and Regression Tree)
• Advantages and Disadvantages and its applications.
• Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART.
• Gini Impurity, Entropy and Information Gain
• Decision Tree Regression
• Visualizing a Decision Tree using graphviz module.
• Regularization using tuning hyper-parameters using GridSearch CV.

Module 15: Classification – Ensemble Methods

• Bootstrap Aggregating or Bagging
• Random Forest algorithm
• Extremely Randomized (Extra-Trees) Ensemble
• Boosting – AdaBoost (Adaptive Boosting), Gradient Boosting
• Machine (GBM), XGBoost (Extreme Gradient Boosting)

Module 16: Unsupervised Learning – Clustering

• Connectivity- based Clustering using Hierarchical Clustering.
• Ward’s Agglomerative Hierarchical Clustering
• K-Means Clustering
• Elbow Method and Solhouette Analysis

Module 17: Unsupervised Learning – Dimensionality Reduction

• Linear Principal Component Analysis (PCA) reduction.
• Kernel PCA
• Linear Discriminant Analysis (LDA) on Supervised Data.

Module 18: Model Deployment On AWS Cloud

• What is cloud computing?
• What is AWS?
• How to store data in AWS S3?
• Create deep learning instance on EC2.
• Amazon sagemaker to train, tune, build and deploy on production.

Module 19: Tableau

• What is tableau? Its Application
• Installing tableau public
• Tableau Application and use
• Tableau tool introduction
• Tableau UI-Dimensions and measures
• Connecting to data
• Filter and its types
• Groups
• Set
• Hierarchy
• Graphs
• Table calculation
• LOD Expression
• Data Blending

Data Science, Deep Learning, & Machine Learning with Python & R Language With Live Machine Learning & Deep Learning Projects

• Project 1 Build your own image recognition model with TensorFlow
• Project 2 Predict fraud with data visualization & predictive modeling!
• Project 3 Spam Detection
• Project 4 Build your own Recommendation System
• Project 5 Build your own Python predictive modeling, regression analysis & machine learning Model
• Getting Started
  • Course Introduction
  • Course Material & Lab Setup
  • Installation
  • Python Basic – Part – 1
  • Python Basic – Part – 2
  • Advance Python – Part – 1
  • Advance Python – Part – 2

●      Statistics and Probability Refresher, and Python Practice

• Types of Data
• Mean, Median, Mode
• Using mean, median, and mode in Python
• Variation and Standard Deviation
• Probability Density Function; Probability Mass Function
• Common Data Distributions
• Percentiles and Moments
• A Crash Course in matplotlib
• Covariance and Correlation
• Conditional Probability
• Exercise Solution: Conditional Probability of Purchase by Age
• Bayes’ Theorem

●      Predictive Models

• Linear Regression
• Polynomial Regression
• Multivariate Regression, and Predicting Car Prices
• Multi-Level Models

●      Machine Learning with Python

• Supervised vs. Unsupervised Learning, and Train/Test
• Using Train/Test to Prevent Overfitting a Polynomial Regression
• Bayesian Methods: Concepts
• Implementing a Spam Classifier with Naive Bayes
• K-Means Clustering
• Clustering people based on income and age
• Measuring Entropy
• Install GraphViz32. Decision Trees: Concepts
• Decision Trees: Predicting Hiring Decisions
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• Using SVM to cluster people using scikit-learn

●      Recommender Systems

• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• Finding Movie Similarities
• Improving the Results of Movie Similarities
• Making Movie Recommendations to People
• Improve the recommender’s results

●      More Data Mining and Machine Learning Techniques

• K-Nearest-Neighbors: Concepts
• Using KNN to predict a rating for a movie
• Dimensionality Reduction; Principal Component Analysis
• PCA Example with the Iris data set
• Data Warehousing Overview: ETL and ELT
• Reinforcement Learning

●      Dealing with Real-World Data

• Bias/Variance Tradeoff
• K-Fold Cross-Validation to avoid overfitting
• Data Cleaning and Normalization
• Cleaning web log data
• Normalizing numerical data
• Detecting outliers

●      Experimental Design

• A/B Testing Concepts
• T-Tests and P-Values
• Hands-on With T-Tests
• Determining How Long to Run an Experiment
• A/B Test Gotchas

●      Deep Learning and Neural Network

●      Statistics and Data Science in R

●      Introduction

• Introduction to R
• R and R studio Installation & Lab Setup
• Descriptive Statistics

●      Descriptive Statistics

• 0Mean, Median, Mode
• Our first foray into R : Frequency Distributions
• Draw your first plot : A Histogram
• Computing Mean, Median, Mode in R
• What is IQR (Inter-quartile Range)?
• Box and Whisker Plots
• The Standard Deviation
• Computing IQR and Standard Deviation in R

●      Inferential Statistics

• Drawing inferences from data
• Random Variables are ubiquitous
• The Normal Probability Distribution
• Sampling is like fishing
• Sample Statistics and Sampling Distributions

●      Case studies in Inferential Statistics

●      Diving into R

• Harnessing the power of R
• Assigning Variables
• Printing an output
• Numbers are of type numeric
• Characters and Dates
• Logicals

●      Vectors

• Data Structures are the building blocks of R
• Creating a Vector
• The Mode of a Vector
• Vectors are Atomic
• Doing something with each element of a Vector
• Aggregating Vectors
• Operations between vectors of the same length
• Operations between vectors of different length
• Generating Sequences
• Using conditions with Vectors
• Find the lengths of multiple strings using Vectors
• Generate a complex sequence (using recycling)
• Vector Indexing (using numbers)
• Vector Indexing (using conditions)
• Vector Indexing (using names)

●      Arrays

• Creating an Array
• Indexing an Array
• Operations between 2 Arrays
• Operations between an Array and a Vector
• Outer Products

●      Matrices

• A Matrix is a 2-Dimensional Array
• Creating a Matrix
• Matrix Multiplication
• Merging Matrices
• Solving a set of linear equations

●      Factors

• What is a factor?
• Find the distinct values in a dataset (using factors)
• Replace the levels of a factor
• Aggregate factors with table()
• Aggregate factors with tapply()

●      Lists and Data Frames

• Introducing Lists
• Introducing Data Frames
• Reading Data from files
• Indexing a Data Frame
• Aggregating and Sorting a Data Frame
• Merging Data Frames

●      Regression quantifies relationships between variables

• Linear Regression in Excel : Preparing the data.
• Linear Regression in Excel : Using LINEST()

●      Linear Regression in R

• Linear Regression in R : Preparing the data
• Linear Regression in R : lm() and summary()
• Multiple Linear Regression
• Adding Categorical Variables to a linear mode
• Robust Regression in R : rlm()
• Parsing Regression Diagnostic Plots

○      Predictive Models

• Linear Regression
• Polynomial Regression
• Multivariate Regression, and Predicting Car Prices
• Multi-Level Models

○      Machine Learning with R

• Supervised vs. Unsupervised Learning, and Train/Test
• Using Train/Test to Prevent Overfitting a Polynomial Regression
• Bayesian Methods: Concepts
• Implementing a Spam Classifier with Naive Bayes
• K-Means Clustering
• Clustering people based on income and age
• Measuring Entropy
• Install GraphViz32. Decision Trees: Concepts
• Decision Trees: Predicting Hiring Decisions
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• Using SVM to cluster people using scikit-learn

○      Recommender Systems

• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• Finding Movie Similarities
• Improving the Results of Movie Similarities
• Making Movie Recommendations to People
• Improve the recommender’s results

○      More Data Mining and Machine Learning Techniques

• K-Nearest-Neighbors: Concepts
• Using KNN to predict a rating for a movie
• Dimensionality Reduction; Principal Component Analysis
• PCA Example with the Iris data set
• Data Warehousing Overview: ETL and ELT
• Reinforcement Learning

○      Dealing with Real-World Data

• Bias/Variance Tradeoff
• K-Fold Cross-Validation to avoid overfitting
• Data Cleaning and Normalization
• Cleaning web log data
• Normalizing numerical data
• Detecting outliers

○      Experimental Design

• A/B Testing Concepts
• T-Tests and P-Values
• Hands-on With T-Tests
• Determining How Long to Run an Experiment
• A/B Test Gotchas

●      Data Visualization in R

• Data Visualization
• The plot() function in R
• Control color palettes with RColorbrewer
• Drawing bar plots
• Drawing a heatmap
• Drawing a Scatterplot Matrix
• Plot a line chart with ggplot

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