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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.

**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 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
- Univariate Timeseries Analysis

# Model Tuning

- Loss Functions
- Noise
- Penalty
- Dimensionality Reduction
- Principal Component Analysis
- r^2 score
- ROC & AUROC
- HyperParameters
- Project Skeletons

# Ensemble Algorithms

- Average
- Weighted
- Conditional
- Bagging
- Boosting

# GitHub Basics

** **

**Principal Component Analysis (PCA)**

- Data Scaling
- Covariance

- Eigen Values
- Eigen Vectors

# Natural Language Processing (NLP)

- Replace
- Spelling Correction
- Named Entity
- Parts of Speech (PoS)
- Text Cleaning
- NGrams
- Tokenization
- RegEx Stemmer
- Singular & Plural
- Translate
- TF-IDF

# NLP Projects

- Anomaly Detection
- Topic Modelling
- Sentiment Analysis
- Auto Tagging
- Spam Classification
- Text Generation – Deep Learning

# Amazon Web Services

- SageMaker

- Bucket Creation
- Regression – Deep Learning
- Classification – Deep Learning

**VlrTraining Address: **

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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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