VLR Training Providing the Python With Machine Learning Online Training by Realtime Scenarios. With Hands-On Live Projects that will Transform you from theoretical knowledge to practical Skill. We also Provide Python with Machine Learning Training Online Class Recordings for Reference Purpose with Lifetime Access.. We also Provide Other Best Software Training Courses Like DevopsAWSDigital MarketingBlockchain, RPA Tools, Data Science, SalesforcePython DjangoFrontend Web Development, Angular etc.

About Python With Machine Learning 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

  • Operators
  • Arithmetic Operators
  • Comparison or Relational Operators
  • Logical or Boolean Operators
  • Bitwise Operators
  • Assignment Operators
  • Special Operators
  • Math Library
  • Variables
  • Data Types
  • Typecasting
  • Booleans
  • Strings
  • Special Characters in a String
  • Split and Strip a String
  • Introduction to Lists
  • Lists Slicing and Reverse Order
  • Kinds of Lists
  • Concatenate Strings Using join() method
  • Add Lists
  • Introduction to Dictionary
  • Necessity of Numpy
  • Creation & Metadata of Numpy Arrays
  • Broadcasting
  • Numpy Built-In Functions
  • Data Types
  • Typecasting
  • Matrix Multiplication
  • Change of Numpy Shape
  • Numpy Slicing
  • Boolean Indexing
  • Filter Data
  • Statistical Methods
  • Sort, Min & Max of Numpy Arrays
  • Stacking & Splitting
  • Copy Vs. View
  • Series
  • DataFrame
  • Metadata
  • Rename Columns & Indices
  • Transpose DataFrame
  • Slice a DataFrame
  • Boolean Indexing
  • Missing Values
  • Replace Values
  • Search, Extract & Create New Columns
  • Set & Unset Index
  • Built-In Customized Functions
  • Value_counts() Method
  • Groupby() & Associated Methods
  • Concat & Append
  • Merge
  • Reshape – Stack & Unstack
  • Pivoting
  • Melt
  • Dummy Variables
  • Crosstab() Method
  • Upper, extract, replace & split Methods
  • Regular Expressions
  • Contains Method
  • StartsWith Method
  • Multiple String Method at a Time
  • Manipulate Column Names
  • Show Columns based on Keyword
  • Read_csv() method
  • Tabbed File
  • Fixed Width Files
  • JSON Data
  • HTML Data
  • XML Data
  • API
  • Export DataFrame to CSV File
  • Encoded Data Files
  • Bad Data
  • Select Columns Based on Datatype
  • How to Convert Non-Timestamp To Timestamp
  • Invalid Data
  • Unix/Epoch Time
  • Datetime Index
  • Current Date Time
  • Date_range & bdate_range Methods
  • Pandas Slicing
  • More components of Datetime
  • Strftime() method
  • Period Range
  • Period
  • Reseample
  • Handle TimeZone
  • One Axis Plot
  • Two Axis Plot
  • Line Style & Color
  • X and Y Limits
  • Line Width
  • Multiple Plots in One Chart
  • Title, X & Y Labels
  • Gridlines
  • Annotations
  • Ticks
  • Spines
  • Legend
  • Subplots
  • Line Plot
  • Bar Graph
  • Scatter Plot
  • Area Plot
  • Box Plot
  • Histogram
  • Pie Chart
  • Count Plot
  • Box Plot
  • Violin Plot
  • Swarm Plot
  • Overlaying Plot of Univariate Variables
  • Facet Grid
  • Lmplot & regplot
  • Size & Shape of a Plot
  • Pair Plot
  • Join Plot
  • Heat Map
  • Types of Data
  • Population Vs Sample
  • Sampling Methods
  • Branches of Statistics
  • Distribution
  • Variance Vs. Standard Deviation
  • Z-Score
  • Correlation
  • Models
  • Probability
  • Labelled Vs Unlabeled Data
  • Types of ML Algorithms
  • How ML Predict things
  • Count Vectorizer
  • Difference between fit and fit_transform Methods
  • Special & Numerical Chracters
  • Remove HTML Tags from Text Data
  • Remove Stop words from Text
  • Stemming
  • Train Test Split
  • Accuracy – MAE, MSE, RMSE & Variance Score
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Classification Algorithms
  • Clustering Algorithms
  • Univariate Timeseries Analysis
  • Loss Functions
  • Noise
  • Penalty
  • Dimensionality Reduction
  • Principal Component Analysis
  • r^2 score
  • ROC & AUROC
  • HyperParameters
  • Project Skeletons
  • Average
  • Weighted
  • Conditional
  • Bagging
  • Boosting

  Principal Component Analysis (PCA)

  • Data Scaling
  • Covariance
  • Eigen Values
  • Eigen Vectors
  • Replace
  • Spelling Correction
  • Named Entity
  • Parts of Speech (PoS)
  • Text Cleaning
  • NGrams
  • Tokenization
  • RegEx Stemmer
  • Singular & Plural
  • Translate
  • TF-IDF
  • Anomaly Detection
  • Topic Modelling
  • Sentiment Analysis
  • Auto Tagging
  • Spam Classification
  • Text Generation – Deep Learning
  • SageMaker
  • Bucket Creation
  • Regression – Deep Learning
  • Classification – Deep Learning

Machine Learning Demo Videos By Dhamodhar Sir

Register Now for Practical Machine Learning Live Demo