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Data Science, Machine Learning & AI Training Course Details

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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.

Data Science, Machine Learning & AI Training Course Benefits

  • Master the essential concepts of Python programming, including data types, tuples, lists, dictionaries and functions.
  • Learn how to write your own Python scripts and perform basic hands-on data analysis using Jupyter notebook
  • Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, visualization, hypothesis building, and testing
  • Perform statical computing using the NumPy and SciPy packages and data analysis with the Pandas package
  • Master the concepts of supervised and unsupervised learning models, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline.
  • Neural Networks Using Tensor Flow.
  • Deep Learning Networks.
  • Deep Learning With Keras.

Duration, Mode & Type

  • 180 Hours/Days (Online Mode)
  • 180 Hours/Days (Offline Mode/Class Room)
  • Week Days Only


  • Basic Knowledge Computer
  • Basic Knowledge Of Excel
  • Knowledge Calculus and Staticstics

Trainer Profile

  • 15+ Years of Experience
  • Conducted 120+ Batches
  • 100% Satisfaction guaranteed

Why Data Science, Machine Learning & AI Training Course At RI-TECH Pune

Theory + Practical’s
15+ Years of Experienced
Faculty from Industry
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100% Job Assurance
Calls till Placements
Hands on Real-time Projects
Extensive Exercise
Free Revision
and Upgrades
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Python Data Science, Machine Learning & AI Training Course Content

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

Python Programming

In this topic, we will learn Python Programming and various features of python.

  • 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?

In this topic we will learn primitive types.

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

In this module, we will try to understand about the various data structures in python.

  • 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

In this module, we will try to understand how to implement conditions & looping.

  • 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

In this module, we will understand the functions in Python Data Science!

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

Learning Objective: In this module, we will learn how to handle Exceptions in python.

  • 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

Learning Objective: understand OOP's programming concepts.

  • 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

Learning Objective: Understand how to read/write with excel, pdf files.

  • 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

Learning Objective: arrange code in modules & packages.

  • 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

Learning Objective: In this module, you will learn various built in modules.

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

Data Analytics & Science

In this module, we will try to understand the basics of data science & analytics.

  • 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

In this module, understand num py library.

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

In this module, we will learn to utilize Pandas library to Analyse data.

  • 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.

In this module, under stand Pivot Tables using pandas in python.

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

In this module, we will try to understand data visualization.

  • 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.

In this module, we will try to understand Data Science Process in detail.

  • 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

In this module, we will implement a real world case study

Machine Learning Training Module Content

In this module, we will learn basics of Machine Learning, Types of Machine Learning.

  • 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

In this module, understand how machine learning works.

  • 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.

In this module, we will learn various types of supervised learning.

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

Hands-on Exercise:
  • 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

In this module, we will try to understand in detail about logistic regression and classification.

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

Hands-on Exercise:
  • 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

In this module, module we want to understand in depth about the Decision Tree and random forest.

  • 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

Hands-on Exercise:
  • 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

In this module, we want to understand Nai ve Bayes theorem and it's implementation using python.

  • 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.

Hands-on Exercise:
  • Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier

In this module, we will learn unsupervised learning with various methods and types.

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

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

In this module, we will learn in depth about Time Series Analysis.

  • 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

Hands-on Exercise:
  • 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

Artificial Intelligence (AI)

In this module, we will learn basics of Artificial Intelligence & Deep Learning.

  • 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

In this module, understand how AI is feature of industry, education and hospitals.

  • 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

In this module, we will learn Tensor Flow.

  • 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

In this module, we will try to understand in detail about 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

In this module, module we want to understand in depth about the Deep Learning Networks.

  • 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)

In this module, we want to understand Deep Learning With Keras.

  • 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

In this module, we will learn Key Applications of AI.

  • Computer Vision
  • Text Data Processing
  • Image processing
  • Audio & video Analytics
  • Internet of things (IOT)
 What is Python & Data Science?

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.

 Who should go for Python Data Science training program?

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

 Who will be the trainers for Python Data Science training Course in Pune?

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.

 Do we need Laptop for Python Data Science training course practical’s in Pune?

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.

  Do you provide all Software’s for Python Data Science training course practical’s in Pune?

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.

  Can I get Job assistance after I complete the Python Data Science training/course in Pune?

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.

  Why should I choose RI TECH Akurdi, Pune-35 for Python Data Science classes in Pune?

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.
Free Technical and Interview Skills.
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.
Latest technology workshops and seminars.

  How can I enroll to Python Data Science training course in Pune?

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

  Do you offer flexible timing for working professionals?

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

  Do you offer demo session for Python Data Science training course in Pune?

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