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**RI-TECH's Data Science, Machine Learning & AI Training Details ** will learn about various AI-based technologies, including machine learning, deep learning, computer vision, natural languages processing, speech recognition, and reinforcement learning. Artificial Intelligence (AI) is the big thing in the technology field and a large number of organizations are implementing AI and the demand for professionals in AI is growing at an amazing speed.

**RI-TECH's Data Science, Machine Learning & AI Training using Python ** is an integrated training course which starts with python basics, gives you in depth knowledge of the Data Science concepts including Data Analysis. It also covers in depth understanding of Machine Learning concepts and explore various Machine Learning Algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning with real world examples from problem definition to creating its model and implement that model using python. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This training also courses various AI technologies like -Tensor Flow, Keras and how to use the these technologies in real world.

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RI-TECH's Python and Data Science, Machine Learning & AI Training Contains following four modules -

- What is Python?
- Installing Python
- Python Interpreter
- Code Editors
- Your First Python Program
- Python Extension
- Lining Python Code
- Formatting Python Code
- Running Python Code
- Python Implementations
- How Python Code is executed?

- Variables
- Variable Names
- Strings
- Escape Sequences
- Formatted Strings
- String Methods
- Numbers
- Working with Numbers
- Type Conversion

- Lists
- Accessing Items
- List Unpacking
- Looping over Lists
- Adding or Removing Items
- Finding Items
- Sorting Lists
- List Comprehensions
- Stacks
- Queues
- Arrays
- Zip Function
- Stacks
- Queues
- Tuples

- Arithmetic Operators
- Comparison Operators
- Conditional Statements, If-else If-elif-else
- Ternary Operator
- Logical Operators
- Short-circuit Evaluation
- Chaining Comparison Operators
- For Loops, For..Else
- Nested Loops
- Iterators
- While Loops
- Infinite Loops
- Filter Function

- What is Function?
- Arguments
- Default Arguments
- *args and **kwargs
- Parameter scope.
- Optional Arguments
- Lambda Functions
- Map Functions
- ZIP Functions

- Errors in Python
- Compile-Time Errors
- Runtime Errors
- Logical Errors
- What is Exception?
- Handling an exception
- Try …except…else
- try-finally clause
- The argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions

- Overview of OOP
- The self-variable
- Constructor
- Types Of Variables
- Namespaces
- Creating Classes and Objects
- Inheritance
- Types of Methods
- Instance Methods Static Methods Class Methods
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- Abstract classes and Interfaces
- Abstract Methods and Abstract class
- Interface in Python
- Abstract classes and Interfaces

- File Handling in Python.
- Read/Write Files
- Read/Write Excel Using xlrd
- Read/Write CSV using Pandas
- Excel Files using Pandas
- Read PDF using PyPDF2

- Import statement
- From import statements
- Reload Module
- Dir function
- What is Packages?
- Intra-package References
- Python Package Index
- The dir Function
- Diff between Package & Module
- Pypi, Pip, Anaconda, Pipenv

- Dates Module
- Math Module
- Statistics module
- NumPy
- Json Module
- RegEx Module
- Random Module

- What Data Analytics?
- Why Data Analytics?
- Tools for Data Analytics
- What is Data Science?
- What does Data Science involve?
- Business Intelligence vs Data Science
- Tools of Data Science
- Installing Jypyter
- Python Basics using Jupyter

- What is NumPy?
- How to install NumPy?
- Arrays Revisited
- NumPy Arrays
- NumPy Operations
- Broadcasting NumPy Array
- NumPy Mathematics/Statics

- Introduction
- Read/Write CSV File
- CSV Analysis
- Data Sets , Data Cleaning
- What is Series? Create Series, Series Operation
- What is Data Frames?
- Diff between Series & Data Frames
- Create Data Frames, Data Frames Operations
- Group, Joins, Concat, Shifting, Melt, Stacking, Un Stacking.

- What is Pivot Tables?
- Why Pivot Tables?
- Create Pivot Table
- Cross Tabs
- Time Series Analysis

- What is Visualization?
- Why Visualization?
- Introduction To Matplotlib
- Bar Graphs, Histogram, Scatter Plot, Area Plot, Pie Plot.
- Introduction To Pandas Visualization
- Bar Graphs, Histogram, Scatter Plot, Area Plot, Pie Plot using Pandas
- Introduction to Seaborn
- Scatter, Hue, Pie using Seaborn.

- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Univariate Analysis
- Bivariate Analysis
- Visualization of Data

- Why We Need Machine Learning
- What is Machine Learning?
- Difference Between AI, ML & Deep Learning
- Types of Machine Learning
- Supervised Learning
- UnSuperviased Learning
- ReInforcement Learning
- Application of Machine Learning

- Collect Data for Machine Learning
- Apply Cleaning, Data Wangling, Reduce Nulls
- Load Data For ML Projects
- Understand Data with Statistics
- Understand Data with Visualization
- Select Feature Selection
- Implement a Algorithm
- Create & Test Model
- Verify the Accuracy of Model
- Refine Model
- Create a solution for IT.

- What is supervised Learning?
- Introduction to regression
- Simple linear regression
- Multiple linear regression and assumptions in linear regression
- Math behind linear regression

- Implementing linear regression from scratch with Python
- Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
- Implementing train–test split and predicting the values on the test set

- Introduction to classification
- Linear regression vs logistic regression
- Math behind logistic regression
- Sigmoid Function
- Confusion Matrix & Accuracy.
- false positive and true positive.

- Implementing logistic regression from scratch with Python
- Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
- Building a confusion matrix to find out accuracy, true positive rate, and false positive rate

- Introduction to tree-based classification
- Understanding a Decision Tree in detail.
- Decision Tree Concepts like impurity function, entropy, information gain.
- Decision Tree Concepts like Gini index, over fitting, Pre-pruning, post-pruning, and cost-complexity Pruning.
- Introduction to Random Forest.
- Random Forest Concepts like -ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest

- Implementing a decision tree from scratch in Python
- Using Python library Scikit-Learn to build a decision tree and a random forest
- Visualizing the tree and changing the hyper-parameters in the random forest

- Introduction to probabilistic classifiers
- Understanding Naïve Bayes
- Math behind the Bayes theorem
- Understanding a support vector machine (SVM).
- Kernel functions in SVM and math behind SVM.

- Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier

- Types of unsupervised learning
- Clustering and dimensionality Reduction
- Introduction to k-means clustering
- Math behind k-means
- Dimensionality reduction with PCA

- Using Python library Scikit-Learn to implement k-means clustering
- Implementing PCA (principal component analysis) on top of a dataset

- What is time series?
- Its techniques and applications
- Time series components
- Moving average, smoothing techniques, and exponential smoothing
- Univariate time series models
- Multivariate time series analysis
- ARIMA model and time series in Python
- Sentiment analysis in Python

- Analysing time series data
- The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
- Forecasting the future values in the series

- Introduction to Deep Learning
- What are the Limitations of Machine Learning?
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
- Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- Introduction To Artificial Intelligence (AI)
- History of AI

- How is this era of AI different?
- Transformative Changes
- Role of Machine learning & Deep Learning in AI
- Hardware for AI (CPU vs. GPU vs. TPU)
- Software Frameworks for AI

- What is Tensor Flow?
- Tensor Flow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Tensorflow Basic Operations
- Linear Regression with Tensor Flow
- Logistic Regression with Tensor Flow
- K Nearest Neighbor algorithm with Tensor Flow
- K-Means classifier with Tensor Flow
- Random Forest classifier with Tensor Flow

- Quick recap of Neural Networks
- Activation Functions, hidden layers, hidden units
- Illustrate & Training a Perceptron
- Important Parameters of Perceptron
- Understand limitations of A Single Layer Perceptron
- Illustrate Multi-Layer Perceptron
- Back-propagation – Learning Algorithm
- Understand Back-propagation – Using Neural Network Example
- TensorBoard

- What is Deep Learning Networks?
- Why Deep Learning Networks?
- How Deep Learning Works?
- Feature Extraction
- Working of Deep Network
- Training using Backpropagation
- Variants of Gradient Descent
- Types of Deep Networks
- Feed forward neural networks (FNN)
- Convolutional neural networks (CNN)
- Recurrent Neural networks (RNN)
- Generative Adversal Neural Networks (GAN)
- Restrict Boltzman Machine (RBM)

- Define Keras
- How to compose Models in Keras?
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization?
- Saving and Loading a model with Keras
- Customizing the Training Process
- Using TensorBoard with Keras
- Use-Case Implementation with Keras
- Intuitively building networks with Keras

- Computer Vision
- Text Data Processing
- Image processing
- Audio & video Analytics
- Internet of things (IOT)

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application. Python provide great functionality to deal with mathematics, statistics and scientific function. It provides great libraries to deals with data science application.

Any Working professional working as application developer wants to grow in his /her carrier. Any Web Designer/ UI Or UX designer want to grow in his/her carrier. Any computer graduate/post graduate who is looking for long term career front in development. BI Managers and Project, Software Developers and ETL Professionals, Analytics Professionals, Big Data Professionals those who wish to have a career in Python

RI-TECH has trainers having 15+ years of IT experience in Software Development and Trainings on various technologies like Knockout, Angular Js, Python Data Science, React etc.

If you want to go for online mode then you need a laptop otherwise do not need Laptop since RI-TECH has it's own fully equipped lab with all necessary infrastructure where you can do the practical’s, but if you want to use your own laptop you can.

We provide you all software's and installation support if you want to install it on your laptop or desktop if you want to do the practice at home.

RI-TECH has proven track records in placements; more than 2000+ students are working in Top MNC's. We provide 100% life time placement support.

15+ years of Experience in Trainings.

Trained to more than 2400+ Students.

More than 2000+ Students Working in MNC's.

Free Upgrade's to Latest Technologies and Release.

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State Of Art Infrastructure.

Exposure to Industry Standards.

RI-TECH provides 100% Placement Assistance.

RI-TECH provides Study Material Designed by Experts having more than 10+ years of experience.

RI-TECH has tie up's with more than 100+ companies for placements & industrial trainings.

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You can do this course online or offline both mode, you just call our executive on Mob. +91 8793801215,+91 9730010404.

Yes, we do, we have flexible timings in week days and weekend for working professionals.

Yes, we do provide One Week(5 Session) demo session for Python Data Science training course in Pune.