Python With Machine Learning Online Training Damodhar

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 Devops, AWS, Digital Marketing, Blockchain, RPA Tools, Data Science, Salesforce, Python Django, Frontend Web Development, Angular etc.,

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data science and machine learning online training

 

 

 

Python With Machine Learning Online Training Damodhar

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.

 

Please Register For DataScince Machine Learning Training

data science and machine learning online training

 

WHAT IS PYTHON WITH MACHINE LEARNING ?

Introduction To Machine Learning using Python.
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

Who this Python With Machine Learning Course is for:

• 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 : 

1. How to use computer science techniques to build the foundation of artificial intelligence, big data, and predictive models.
2. How to use scikit-learn, a powerful tool, to comb over your available data and implement practical machine learning techniques.
3. How to use Pandas and NumPy to accomplish various data mining and data wrangling tasks to turn your data into useable training data.
4. How to build basic deep neural networks that represent the cutting-edge when it comes to reinforcement learning and deep learning in machines.
5. 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.

Python With Machine Learning Training Course Content : 

 

Introduction

  • Course Overview
  • Installation of Anaconda
  • Jupyter Notebook Basics
  • DataSets
  • Trello Overview
  • Git, GitHub Basics
  • Slack

 

Python Programming

  • 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
  • Dictionary and It’s Methods
  • Nested Dictionary
  • Create Dictionary Using zip() method
  • Tuples
  • Set
  • If Condition
  • While Loop
  • Range() Method
  • For Loop
  • Reserve Keywords
  • Built-In Functions
  • User Defined Functions
  • Anonymous or Lambda Functions
  • File IO Operations

 

Numpy

  • 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

 

Pandas

  • 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

 

Time Series Analysis

  • 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

 

Matplotlib

  • 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

 

Seaborn

  • 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

 

Statistics

  • Types of Data
  • Population Vs Sample
  • Sampling Methods
  • Branches of Statistics
  • Distribution
  • Variance Vs. Standard Deviation
  • Z-Score
  • Correlation
  • Models
  • Probability

 

Machine Learning Basics

  • 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

 

Projects

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Classification Algorithms
  • Clustering Algorithms

 

ML Basics (Contd…)

  • Underfitting Vs. Overfitting
  • Noise
  • L1
  • L2

 

Hyperparameters

  • n_estimators
  • max_features
  • learning_rate
  • max_depth
  • C
  • kernel
  • gamma
  • criterion
  • splitter
  • random_state
  • min_samples_split
  • max_iter
  • dual
  • min_samples_leaf
  • p
  • n_neighbors
  • metric
  • fit_prior
  • priors

 

Metrics

  • Classification
    • Accuracy
    • Logarithmic Loss
    • Area Under ROC Curve
    • Confusion Matrix
    • Classification Report
  • Regression
    • Mean Absolute Error
    • Mean Squared Error
    • R^2

 

Skeletons

  • Regression
  • Classification
  • Clustering

 

Dimensionality Reduction

  • Missing Value Ratio
  • Low Variance Filter
  • High Correlation Filter
  • Random Forest
  • Backward Feature Elimination
  • Forward Feature Selection
  • Factor Analysis
  • Principal Component Analysis
  • Independent Component Analysis
  • Methods Based on Projections
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • UMAP

Projects

• Demo Projects – 3 to 4 Projects on Regression & Classification
• Guided Projects – 2 to 3 Projects
• Assignment Projects – 5 Projects

 

VlrTraining Address:
PlotNo 126/b,2nd floor, Street Number 4,
Addagutta Society, Near Jntuh,Pragarthi Nagar Road,
Kukatpally, Hyderabad, Telangana 500072
Name: Python With Machine Learning Online Training Damodhar
Telephone:9059868766
Opening hours:7 Am to 9 Pm (IST)

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