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**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 ?**

**Introduction To Machine Learning using Python.**

**Who this Python With Machine Learning Course is for:**

**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

**Python Programming**

• Operators

o Arithmetic Operators

o Comparison or Relational Operators

o Logical or Boolean Operators

o Bitwise Operators

o Assignment Operators

o 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

**Model Tuning**

• Alpha

• L1 Ratio

• N Estimators

• Max Features

• Learning Rate

• Max Depth

• C

• Kernel

• Gamma

• Criterion

• Splitter

• Random State

• Min Samples Split

• Max Iterations

• Dual

• Min Samples Leaf

• P

• N Neighbors

• Metric

• Alpha

• Fit Prior

• Priors

**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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