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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.

## Please Register For DataScince Machine Learning Training

**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

->Trello Overview

->Slack**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

->Underfitting Vs. Overfitting

->Noise

->L1

->L2**Basic Projects**

->Simple Linear Regression

->Multiple Linear Regression

->Polynomial Regression

->Classification Algorithms

->Clustering Algorithms

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

o Accuracy

o ROC Curve

o Area Under ROC Curve

o Confusion Matrix

o Classification Report

->Regression

o Mean Absolute Error

o Mean Squared Error

o R^2**Dimensionality Reduction**

->Filter Methods

->Wrapper Methods

->Embedded Methods

->Principal Component Analysis

Skeletons

->Regression

->Classification

->Clustering

Projects

->Projects on Regression & Classification

**Deep Learning**

->Neural Network

->Forward Propogation

->Backward Propogation

->Optimizer

->Keras

->CNN

->RNN

->Padding

->Pooling

->LSTM

NLP Projects

->5 Projects on NLP with Deployment

**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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