Data Science Machine Learning Training Courses

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 Tensor Flow
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 outliersa
○ 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