Machine Learning with Python, starts 29th July 2023. Only 15 seats vacant Book Now x

INFYNAS

Infynas

TRAINING | CERTIFICATION | PLACEMENT

Master’s In Data Science – Fully Practical Program!

Step-by-step guidance to Data Science and Machine Learning!

100% Placement Assistance – Learn Data Science and win job opportunities in no time!

Enroll Now

    Duration

    90+ Hours of Comprehensive Live Sessions including Industry-Based Assessments and Projects.

    Mode

    Online / Offline

    Validity

    6 Months from Purchase

    Eligibility

    Freshers, Any Graduates, Any Post-Graduate, Any Engineers, Working Professionals, Tech Enthusiasts, or Entrepreneurs.

    Introduction

    Data Science & machine learning is acclaimed as one of the best career opportunities with application in practically every sector of professional activities – e.g., e-commerce, manufacturing, IOT and robotics, medical field, security to fashion. This Data Scientist course aims to accelerate your career in Data Science and provides you with world-class training and skills required to become successful in this field. The Data Scientist course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, Data Visualization, Statistics, concepts of Machine Learning and introduction of Deep Learning.

    Our Data Science course is specially designed by industry experts with more than 15 years of industry experience, covering the complete Data Science concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and Model Deployment. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine LearningDeep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Python and R programming.

    Python are covered extensively as part of this Data Science training. Python has become number one choice for data science, Machine Learning & Artificial Intelligence applications. The training is a practical oriented, step by-step guidance to Data Science using Python.

    Learning Outcome

    After the completion of training, the participants would be able to:

    Why Should You Choose INFYNAS For Data Science Training?

    Our Data Science course is specially designed by industry experts and if you are serious about a career pertaining to Data science, then you are at the right place. Infynas Learning Solutions is considered to be one of the best Data Science training institutes. We have built careers for lot of Data Science professionals in various MNCs. “Training to Job Placement” – is our forte. We do the necessary hand-holding until you are placed. Our expert trainers will help you with upskilling the concepts, to complete the assignments and live projects. Our trainers are experienced professionals and currently working with top MNCs who keep live projects that assist pupils to learn in a much better manner. We ensure that the students work on live jobs so that they learn and relate things quickly and improve their skills well. We have a dedicated placement team who work with partners to facilitate the interviews to help the participants getting placed.

    Course Highlights

    • blue star Specially designed curriculum
    • orange star Industry Expert Trainers
    • blue star Learn From Top Data Scientist
    • orange star Online learning session with live instructor-led training
    • blue star One-on-one student guidance support
    • orange star Learn at your own pace
    • blue star Easy & Convenient learning style
    • orange star All-time Academic support throughout the course
    • blue star Hassle-free access to course
    • orange star Opportunity to work on file formats on different data
    • blue star Excellent understanding of Data Science Algorithms
    • orange star Real-Time Projects & Case Studies
    • blue star Webinar / Workshop
    • orange star Internship & Placement opportunities

    Who is This Course For?

    Individuals and Professionals who can consider Data Science course as a next logical move to enhance their careers. We welcome Freshers from any stream, Any Graduates, Any Post-Graduate, Any Engineers, Working Professionals from any domain, Tech Enthusiasts, Entrepreneurs, and those who see the future in the Data Science industry. There’s a lot behind learning the Data science, because of its vast scope and it is expected to be the most attractive industry in the coming generations

    Course Objectives

    Understand what is Machine Learning and its applications

    Understanding of Data Science process and workflow

    Understand different types of learning algorithms

    Understand process and need for Data Preparation & Data Wrangling

    Build models practically using python libraries

    Using different evaluation metrics to test model performance

    Learn Statistics for Data Science, Predictive Modeling & Analytics

    Learn How to Visualize the Data and Models

    Understand Concept of Deep Learning

    Course Curriculum

    • Introduction to the course
    • Introduction to Data Science
    • Introduction to Machine Learning
    • Introduction to Analytics
    • What does data science involves
    • Life cycle of Data Science
    • Design Thinking and Problem Statement
    • Tools of Data Science
    • Relationship between Artificial Intelligence, Machine Learning, and Data Science
        o Major classes of Learning Algorithms
        o Areas of implementation of Machine learning
      o Supervised vs. Unsupervised learning

     

    • Installation of necessary tools /libraries
    • Using Jupyter Notebook
    • Python environment Setup and Essentials
    • Introduction to python
    • Software installation
    • Basic operators and functions
    • Python Variables: int, float, string, bool, complex
    • Data types with python
    • Conditional statement
    • Loops
    • Python Collections: List, Tuple, Dictionary, Set, Frozenset
    • Functions and Methods
    • Class & Objects
    • Error Handling
    • Regular Expression
    • Introduction to Numpy
    • Datatypes
    • Introduction to numpy arrays
    • How to Access Array Elements?
    • Indexing, Slicing, Iteration, Indexing with Boolean Arrays
    • Dealing with Flat fles using numpy
    • Mathematical functions
    • Statistical functions (mean, median, average, standard deviation)
    • Operations with arrays
     
    • Scipy subpackage
    • Linalg
    • Special
    • stats
    • Types of statistics
    • Types of Data
    • The measure of Central Tendency
    • Five number summery boxplot
    • Measure of Dispersion
    • Skewness and Kurtosis
    • Covariance and Correlation
    • Central Limit Theorem
    • Introduction to Hypothesis Testing
    • Data and Types
    • Central Tendency: Mean, Median, Mode
    • Deviation: Range, Variance, Standard Deviation
    • BoxPlot and its importance
    • Frequency distribution and its importance
    • Scatter Plots and its importance
    • Probability
    • Discrete Probability Distribution: Binomial Distribution, Poisson
    • Continuous Probability Distribution: Normal, t distribution, Exponential
    • Correlation

    SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database.

    • Introduction to Pandas
    • Defining data structures
    • Pandas datatypes
    • Creating Data frames
    • Data frame operations
    • Indexing / reindexing –loc, iloc
    • Grouping, Sorting, Slicing/Dicing operations
    • Binning
    • Aggregations
    • Filtering
    • Merging/Joining
    • Date Functionality
    • Importing /exporting data
    • Importing data from csv excel json web database etc.
    • Cleaning and preparing data
    • Normalization and Standardization of data
    • Encoding
    • Handling missing data
    • Quantitative Technique
    • Graphical Technique
    • Outlier Values in a Dataset
    • Splitting the Dataset into the Training set and Test set
    • Automated tools for EDA- pandas profiling ,sweetviz
    • Data visualization with pandas
    • Visualization using Matplotlib and Seaborn
    • Bar Graph
    • Histogram
    • Scatter Plot
    • Area Plot
    • Pie Chart
    • Special plots
    • Violin Plot
    • Boxplot
    • Heatmap
    • Facet Grid Pair grid
    • Adding legend & text
    • Color palette

    Regression Analysis

    • Regression Algorithms
    • Linear Regression
    • Logistic regression
    • Regression Use Case
    • Cost Function
    • Evaluating Coefficients
    • Evaluating Regression Models Performance
    • Accuracy Matrix
    • R-Squared
    • Interpreting Linear Regression Coefficients

     

    Regularization – introduction

    • Lasso and Ridge Regression
    • Elastic Net Regularization

     

    K-NN classification

    • Introduction and working
    • Implementation using Scikit-Learn

     

    Naïve Bayesian classifiers

    • Intro to Bayesian theorem
    • Implement NB through Scikit-Learn

     

    SVM – (Support Vector Machines)

    • Introduction to Support Vector Machines
    • Python Kernel SVM
    • The Kernel Trick
    • Types of Kernel Functions
    • SVM Algorithm for Classification
    • SVM for Regression

     

    Decision Tree

    • Entropy and information gain
    • Ginny impurity
    • Visualizing Decision tree
    • Regression tree and classification trees

     

    Clustering

    • Clustering
    • K-means Clustering
    • Finding Optimal Number of Clusters

     

    Association Rule Mining

    • Rule Mining Market Basket Analysis
    • Rule Generation with Apriori Algorithm

     

    Model Selection

    • Cross Validation

     

    Hyper parameter tuning

    • Hyperparameter tuning techniques

     

    Time series modeling

    • ARMA
    • ARIMA
    • SARIMA

     

    Dimensionality Reduction Techniques

    • Component Analysis (PCA)
    • LDA

     

    Ensemble Techniques

    • Intro to ensemble Techniques
    • Bagging
    • Boosting
    • Stacking
    • RandomForest
    • AdaBoost
    • GradientBoosting
    • XGboost

    Working with imbalanced data

    • smote

     

    Explainable ML

    • Introduction and need for
    • Getting insight with eli5 shap and lime

     

    Deploy your model

    • Deploy your model locally
    • Basic of flask
    • Deploy your model as flask web app
    • Create dashboard with streamlit

     

    Visualization With Tableau

    • Introduction to Visualization
    • Connect to your data
    • Concepts: Filter, Join, Hierarchy, Groups, Set
    • Explore your data geographically
    • Drill down into the details
    • Build a dashboard to show your insights
    • Build a story to present

     

    Introduction to Deep Learning

    • TensorFlow
    • Neural Network – ANN, CNN, RNN
    • Autoencoders
    • Long Short-term memory (LSTM)
    • Restricted Boltzman Machine (RBM)
    • NLP Overview
    • Introduction to R and RStudio
    • RStudio interface and basics
    • Basic arithmetic and variable assignment
    • Comparison and logical operators
    • Data types
    • Vectors
    • Functions
    • Loops and conditionals
    Jira helps teams plan, assign, track, report, and manage work and brings teams together for everything from agile software development and customer support to start-ups and enterprises.

    GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere. This tutorial teaches you GitHub essentials like repositories, branches, commits, and pull requests.

    • Case study-based approach
    • 5 Projects in multiple domain
    • Customer Segmentations on Sales Dataset.
    • Financial Risk Modeling on German Credit Dataset
    • House Pricing Prediction Modeling
    • Loan Approval Prediction on Customer Dataset
    • Predicting the price for used car
    1. Mock interview
    2. Interview Preparation
    3. Resume Preparation
    4. Placement Assistance

    30+

    Assignments

    15+

    Projects of different domain

    1 to 3

    Capstone Projects

    No.1

    Teaching Methodology

    Course Benefits

    Learn how to implement data science, machine learning and artificial intelligence techniques and algorithms & providing solutions to real-life problems within organization.

    Develop an indepth understanding of data science, machine learning and artificial intelligence concepts and identify the best models to fit various business solutions.

    This course connects the academic and industry by bridging the gaps for the new aspirants.

    Interact and collaborate with industry expert trainer and mentor to understand the technology and applications of Data Science, Machine Learning & Artificial Intelligence

    Gain Hands-on-oriented sessions blended with elegant explanations of core concepts, real and relevant examples help candidates to learn faster and start developing projects and applications.

    This course will help candidates to understand the concept of data science and build the flow and competence that is expected by job markets and industries.

    Acts as an enabler to the working professionals to boost up their careers, and provide an extra edge

    Our flexible and dedicated support teams help candidates to never stop learning and practicing by getting their doubts clarified in shorter response timelines and making them quickly move on further.

    Get Step-By-Step Guidance To Data Science With Python

    • Introduction to the course
    • Introduction to Data Science
    • Introduction to Machine Learning
    • Introduction to Analytics
    • What does data science involves
    • Life cycle of Data Science
    • Design Thinking and Problem Statement
    • Tools of Data Science
    • Relationship between Artificial Intelligence, Machine Learning, and Data
    • Science
      • Areas of implementation of Machine learning
      • Major classes of Learning Algorithms
      • Supervised vs. Unsupervised learning
    • Installation of necessary tools /libraries
    • Using Jupyter Notebook
    • Python environment Setup and Essentials
    • Introduction to python
    • Software installation
    • Basic operators and functions
    • Python Variables: int, float, string, bool, complex
    • Data types with python
    • Conditional statement
    • Loops
    • Python Collections: List, Tuple, Dictionary, Set, Frozenset
    • Functions and Methods
    • Class & Objects
    • Error Handling
    • Regular Expression
    • Introduction to Numpy
    • Datatypes
    • Introduction to numpy arrays
    • How to Access Array Elements?
    • Indexing, Slicing, Iteration, Indexing with Boolean Arrays
    • Dealing with Flat fles using numpy
    • Mathematical functions
    • Statistical functions (mean, median, average, standard deviation)
    • Operations with arrays
    • Scipy subpackage
    • Linalg
    • Special
    • stats
    • Types of statistics
    • Types of Data
    • The measure of Central Tendency
    • Five number summery boxplot
    • Measure of Dispersion
    • Skewness and Kurtosis
    • Covariance and Correlation
    • Central Limit Theorem
    • Introduction to Hypothesis Testing
    • Data and Types
    • Central Tendency: Mean, Median, Mode
    • Deviation: Range, Variance, Standard Deviation
    • BoxPlot and its importance
    • Frequency distribution and its importance
    • Scatter Plots and its importance
    • Probability
    • Discrete Probability Distribution: Binomial Distribution, Poisson
    • Continuous Probability Distribution: Normal, t distribution, Exponential
    • Correlation
    • Introduction to Pandas
    • Defining data structures
    • Pandas datatypes
    • Creating Data frames
    • Data frame operations
    • Indexing / reindexing –loc, iloc
    • Grouping, Sorting, Slicing/Dicing operations
    • Binning
    • Aggregations
    • Filtering
    • Merging/Joining
    • Date Functionality
    • Importing /exporting data
    • Importing data from csv excel json web database etc.
    • Cleaning and preparing data
    • Normalization and Standardization of data
    • Encoding
    • Handling missing data
    • Quantitative Technique
    • Graphical Technique
    • Outlier Values in a Dataset
    • Splitting the Dataset into the Training set and Test set
    • Automated tools for EDA- pandas profiling ,sweetviz
    • Data visualization with pandas
    • Visualization using Matplotlib and Seaborn
    • Bar Graph
    • Histogram
    • Scatter Plot
    • Area Plot
    • Pie Chart
    • Special plots
    • Violin Plot
    • Boxplot
    • Heatmap
    • Facet Grid Pair grid
    • Adding legend & text
    • Color palette

    Regression Analysis

    • Regression Algorithms
    • Linear Regression
    • Logistic regression
    • Regression Use Case
    • Cost Function
    • Evaluating Coefficients
    • Evaluating Regression Models Performance
    • Accuracy Matrix
    • R-Squared
    • Interpreting Linear Regression Coefficients

    Regularization – introduction

    • Lasso and Ridge Regression
    • Elastic Net Regularization

    K-NN classification

    • Introduction and working
    • Implementation using Scikit-Learn

    Naïve Bayesian classifiers

    • Intro to Bayesian theorem
    • Implement NB through Scikit-Learn

    SVM – (Support Vector Machines)

    • Introduction to Support Vector Machines
    • Python Kernel SVM
    • The Kernel Trick
    • Types of Kernel Functions
    • SVM Algorithm for Classification
    • SVM for Regression

    Decision Tree

    • Entropy and information gain
    • Ginny impurity
    • Visualizing Decision tree
    • Regression tree and classification trees

    Clustering

    • Clustering
    • K-means Clustering
    • Finding Optimal Number of Clusters

    Association Rule Mining

    • Rule Mining Market Basket Analysis
    • Rule Generation with Apriori Algorithm

    Model Selection

    • Cross Validation

    Hyper parameter tuning

    • Hyperparameter tuning techniques

    Time series modeling

    • ARMA
    • ARIMA
    • SARIMA

    Dimensionality Reduction Techniques

    • Component Analysis (PCA)
    • LDA

    Ensemble Techniques

    • Intro to ensemble Techniques
    • Bagging
    • Boosting
    • Stacking
    • RandomForest
    • AdaBoost
    • GradientBoosting
    • XGboost

    Working with imbalanced data

    • smote

    Explainable ML

    • Introduction and need for
    • Getting insight with eli5 shap and lime

    Deploy your model

    • Deploy your model locally
    • Basic of flask
    • Deploy your model as flask web app
    • Create dashboard with streamlit
    • Case study-based approach
    • 5 Projects in multiple domain
    • Customer Segmentations on Sales Dataset.
    • Financial Risk Modeling on German Credit Dataset
    • House Pricing Prediction Modeling
    • Loan Approval Prediction on Customer Dataset
    • Predicting the price for used car
    1. Mock interview
    2. Interview Preparation
    3. Resume Preparation
    4. Placement Assistance
    Open chat
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