

Data Science and Machine Learning Using Python | R
Learn Data Science & Machine Learning, Deep Learning with Python Or R Language
Trainer :- Experienced Data Science Consultant
Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays
Become a Data Scientist
One time class room registration to Payment Details Fee 1000/-
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
- 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
- 1. Invoking the Interpreter
- 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
- 1. Using Python as a Calculator
- 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
- 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
- 1. More on Lists
- 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
- 1. More on Modules
- 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
- 1. Fancier Output Formatting
Data Science with Python
- Install Anaconda Distribution as per OS from https://www.anaconda.com/distribution/ (Python 3.7 version)
- Sign Up for account creation on https://www.hackerrank.com/
- Sign up for account creation on https://www.kaggle.com/
- Sign up for account creation on https://github.com/
- Git Bash Utility – https://git-scm.com/downloads
Module 1: Statistics and Probability
- 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
- 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
- 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
- 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
- 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
- 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.
- Plotly and Cufflinks
- Interactive Plotting using Plotly and Cufflinks
- 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
We Will Be Updated Soon.
We Will Be Updated Soon.
We Will Be Updated Soon.
- + Introduction
-
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
- 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
- 1. Invoking the Interpreter
- 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
- 1. Using Python as a Calculator
- 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
- 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
- 1. More on Lists
- 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
- 1. More on Modules
- 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
- 1. Fancier Output Formatting
Data Science with Python
- Install Anaconda Distribution as per OS from https://www.anaconda.com/distribution/ (Python 3.7 version)
- Sign Up for account creation on https://www.hackerrank.com/
- Sign up for account creation on https://www.kaggle.com/
- Sign up for account creation on https://github.com/
- Git Bash Utility – https://git-scm.com/downloads
Module 1: Statistics and Probability
- 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
- 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
- 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
- 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
- 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
- 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.
- Plotly and Cufflinks
- Interactive Plotting using Plotly and Cufflinks
- 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
- Using the Python Interpreter
- + Curriculum
-
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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