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AI with R & Python Vlr training

VLR Training Providing the Advanced Machine Learning with Python Online Training by Realtime Scenarios. With Hands-On Live Projects that will Transform you from theoretical knowledge to practical Skill. We also Provide Advanced Machine Learning with Python training Online Class Recordings for Reference Purpose with Lifetime Access.. 

About Advanced Machine Learning with R & Python Online Course

This Machine Learning with Python course dives into the basics of Machine Learning using Python, an approachable and well-known programing language. You’ll study supervised vs unsupervised Learning, examine however statistical Modeling relates to Machine Learning, and do a comparison of every. Look at real-life samples of Machine Learning and the way it affects society in ways that you will not have guessed! Explore many algorithms and models: • Popular algorithms: Regression, Classification, Dimensional Reduction, Clustering. • Popular models: Root Mean Squared Error, Train/Test Split, and Random Forests. More important, you will transform your theoretical knowledge in to practical skill using many hands-on projects.

What Is Python With Machine Learning?

Machine learning is a kind of artificial intelligence (AI) that gives computers with the flexibility to be told withoutbeing expressly programmed.
Machine learning focuses on the development of laptop Programs that may change once exposed to new information

Prerequisites for Python With Machine Learning

• Anyone interested to learn machine learning algorithm with Python • Who includes a deep interest within the usage of machine learning to real world issues • Anyone wishes to move beyond the basics and develop an understanding of the whole machine learning algorithms • Any intermediate to advanced EXCEL users who is unable to work with large datasets • Anyone interested to present their findings in a professional and convincing manner • Who needs to begin or transit into a career as a knowledge someone • Anybody wants to apply machine learning to their domain

What will you learn In This Python Machine Learning Online Training

  • How to use computer science techniques to build the foundation of artificial intelligence, big data, and predictive models.
  • How to use scikit-learn, a powerful tool, to comb over your available data and implement practical machine learning techniques. 
  • How to use Pandas and NumPy to accomplish various data mining and data wrangling tasks to turn your data into useable training data.
  • How to build basic deep neural networks that represent the cutting-edge when it comes to reinforcement learning and deep learning in machines. 
  • The most common supervised learning and unsupervised learning algorithms, from linear regression to logistic regression to k-means clustering to random forest and other decision tree techniques.

Practical Machine Learning. DeepLearning ,NLP with Python content

Download course Content Practical Machine Learning. DeepLearning ,NLP with Python Hands On Projects Introduction

  • Course Overview
  • Installation of Anaconda
  • Jupyter Notebook Basics
  • DataSets

Python Machine Learning Course Content

1 Data types
  a. Continuous variables
  b. Ordinal Variables
  c. Categorical variables
  d. Time Series
2. Descriptive statistics
3. Sampling
4. Data distributions
   a. Normal Distribution – Characteristics of a normal distribution
   b. Binomial Distribution
5. Inferential statistics
6 Hypothesis testing

  • What is R?
  • Types of objects in R
  • Creating new variables or updating existing variables
  • IF statements and conditional loops – For, while etc.
  • String manipulations
  • Sub setting data from matrices and data frames
  • Casting and melting data to long and wide format.
  • Merging datasets

a. Getting data into R – reading from files
b. Cleaning and preparing the data – converting data types (Character to numeric etc.)
c. Handling missing values
d. Visualization in R using ggplot2(plots and charts) – Histograms, bar charts, box plot, scatterplots e. Adding more dimensions to the plots
f. Visualization using Tableau( Introduction)
g. Correlation – Positive , negative and no correlation
h. What is a spurious correlation
i. Correlation vs. causation

a. Understanding the reason of Python’s popularity
b. Basics of Python: Operations, loops, functions, dictionaries
c. Advanced operations with text: Finding, Sequencing and basic analytics
d. Ground-up for Deep-Learning

  1. Supervised learning

         Linear Regression (Prediction)

1. Simple Linear Regression

2. Assumptions

3. Model development and interpretation

4. Model validation – tests to validate assumptions

5. Multiple linear regression

6. Disadvantages of linear models

Logistic Regression (Classification)

1.       Need for logistic regression

2.       Logit link function

3.       Maximum likelihood estimation

4.       Model development and interpretation

5.       Confusion Matrix ROC curve

6.       Pros and Cons of logistic regression models


b. Un-Supervised learning

    Cluster analysis (Segmentation)

1. Hierarchical clustering

2. K-Means clustering

3. Distance measures


d. Market Basket Analysis (Association Rule Mining) – Cross Selling


f. K-NN (Nearest Neighbor)

c. Time series analysis – Forecasting

1. Simple moving averages

2. Exponential smoothing

3. Time series decomposition



e. Text Analytics (NLP)

       a. Decision trees

1.       Process of tree building

2.       Entropy and Gini Index

3.       Problem of over fitting

4.       Pruning a tree back

5.       Trees for Prediction (Linear) – example

6.       Tress for classification models – example

7.       Advantages of tree based models?


b. Re-Sampling and Ensembles Methods

1. Bagging – Random Forest

2. Boosting – Gradient boosting machines


c. Advanced methods

1. Support Vector machines

2. Neural networks

3. Introduction to deep learning


a. RMSE – Root Mean squared error
b. Misclassification rate
c. Area under the curve (AUC)

a. Imbalanced Classification problem.
b. High Cardinal data problem
c. Encoding cat and continuous variables
d. Overfitting and Underfitting models

a. Dplyr
b. Lubridate
c. Tidyr
d. Caret
e. Ggplot2
f. Reshape2

a. Introduction to H2o
b. Modelling with H2o on R

a. Interview Preparation
b. Case studies
c. Guidance to prepare resumes
d. Information on companies and industry trends on data science

Machine Learning Demo Videos By Dhamodhar Sir

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