Data Science & Machine Learning Training In Delhi

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    Introduction: of  Data Science  & Machine Learning Course

    If you are starting out in programming the best thing would be begin with Python, As per all the recent employment forecasts it is predicted Data Sciences and Machine Learning   will create most lucrative career options in coming years so it will be wise to give a head-start to your career with disciplined learning in Data Sciences along with Machine learning for a bright and diversifying future. 

    Objective: of Data Science & Machine Learning Classes

    Course covers the necessary tools and concepts used in the data science field which includes machine learning, statistical inference, working with data at scale and etc.

    Student will begin with entire process for data science projects and the different roles and skills that are needed, Obtaining data through a variety of sources, including web APIs and page scraping.  Using tools like Python, Pandas, Numpy, Seaborns, matplotlib, and numerous algorithm to explore and manipulate data.

    Course Schedule: of Data science Course

    Duration: 5- 6 months
    Schedule:
    2 Hours 3 Days a week (Weekdays)
    2 Hours 2 Days a week (Weekends)

    Course Structure:

    Part-1 -Core Python

    1. Module 1:  Getting started with Python programming

      • Overview
      • Introductory Remarks about Python
      • A Brief History of Python
      • How python is differ from other languages
      • Python Versions
      • Installing Python and Environment Setup
      • IDLE
      • Getting Help
      • How to execute Python program
      • Writing your first Python program
      • How to work on different Popular IDE’s    [Pycharm, Jupyter Notebook, Spyder etc.]

      Module 2: Variables, Keywords and Operators

      • Variables
      • Memory mapping of variables
      • Keywords in Python
      • Comments in python
      • Operators
        1. Arithmetic Operators
        2. Assignment Operators
        3. Comparison Operators
        4. Logical Operators
        5. Membership Operators
        6. Identity Operators
        7. Bitwise Operators
      • Basics I/O and Type casting
      • Getting user input

      Module 3: Data types in Python

      • Numbers
      • Strings
      • Lists
      • Tuples
      • Dictionary
      • Sets

      Module 4: Numbers and Strings

      • Introduction to Python ‘Number’ & ‘string’ data types
      • Properties of a string
      • String built-in functions
      • Programming with strings
      • String formatting

      Module 5: Lists and Tuples

      • Introduction to Python ‘list’ data type
      • Properties of a list
      • List built-in functions
      • Programming with lists
      • List comprehension
      • Introduction to Python ‘tuple’ data type
      • Tuples as Read only lists

      Module 6: Dictionary and Sets

      • Introduction to Python ‘dictionary’ data type
      • Creating a dictionary
      • Dictionary built-in functions
      • Introduction to Python ‘set’ data type
      • Set and set properties
      • Set built-in functions

      Module 7: Decision making & Loops

      • Introduction of Decision Making
      • Control Flow and Syntax
      • The if Statement
      • The if…else Statement
      • The if…elif…else Statement
      • Nested if…else Statement
      • The while Loop
      • break and continue  Statement
      • The for Loop
      • Pass statement
      • Exercise


      Module 8: User defined Functions

      • Introduction of functions
      • Function definition and return
      • Function call and reuse
      • Function parameters
      • Function recipe and docstring
      • Scope of variables
      • Recursive functions
      • Lambda Functions / Anonymous Functions
      • Map , Filter & Reduce functions

      Module 9: Module s and Packages

      • Module s
      • Importing Module
      • Standard Module – sys
      • Standard Module – OS
      • The dir Function
      • Packages
      • Exercise

      Module 10: Regular expression

      • Pattern matching
      • Meta characters for making patterns
      • re flags
      • Use of match() , sub() , findall(), search(), split() methods

      Part-2 – Data Analysis

      Module 1: GETTING STARTED WITH PYTHON LIBRARIES

      • What is data analysis?
      • Why python for data analysis?
      • Essential Python Libraries Installation and setup
      • Ipython
      • Jupyter Notebook
      Module 2: NUMPY ARRAYS
      • Introduction to Numpy
      • Numpy Arrays
      • Numpy Data types
      • Numpy Array Indexing
      • Numpy  Mathematical Operations
      • Indexing and slicing
      • Manipulating array shapes
      • Stacking arrays
      • Sorting arrays
      • Creating array views and copies
      • I/O with NumPy
      • Numpy Exercises
      Module 3: WORKING WITH PANDAS
      • Introduction to Pandas
      • Data structure of pandas
      • Pandas Series
      • Pandas dataframes
      • Data aggregation with Pandas
      • DataFrames Concatenating and appending
      • DataFrames Joining
      • DataFrames Handling missing data
      • Data Indexing and Selection
      • Operating on data in pandas
      • loc and iloc
      • map,apply,apply_map
      • group_by
      • string methods
      • Querying data in pandas
      • Dealing with dates
      • Reading and Writing to CSV files with pandas
      • Reading and Writing to Excel with pandas
      • Reading and Writing to SQL with pandas
      • Reading and Writing to HTML files with pandas
      • Pandas Exercises

      Part-3 – Data Visualization

      Module 1: Matplotlib

      • Introduction of Matplotlib
      • Basic matplotlib plots
      • Line Plots
      • Bar Plots
      • Pie Plots
      • Scatter plots
      • Histogram Plots
      • Saving plots to file
      • Plotting functions in matplotlib
      • Matplotlib Exercises

      Module 2: Seaborn

      • Introduction of Seaborn
      • Distribution Plots
      • Categorical Plots
      • Matrix Plots
      • Bar Plots
      • Box Plots
      • Strip Plots
      • Violin Plots
      • Clustermap Plots
      • Heatmaps Plots
      • KDE Plots
      • Regression Plots
      • Style and Color
      • Seaborn Exercise

      Module 3:  Plotly and Cufflinks

      • Introduction to Plotly and Cufflinks
      • Plotly and Cufflinks

      Module 4: Geographical Plotting

      • Introduction to Geographical Plotting
      • Choropleth Maps – Part 1
      • Choropleth Maps – Part 2
      • Choropleth Exercises
      • Projects using Analysis and Visualisation

      Part-4 – Machine Learning

      Module 1: Introduction to Machine Learning Course in Delhi  

      • What is Machine learing?
      • Overview about scikit-learn  package
      • Types of ML
      • Basic steps of ML
      • ML algorithms
      • Machine learning examples

      Module 2: Data Preprocessing

      • Dealing with missing data
      • Identifying missing values
      • Imputing missing values
      • Drop samples with missing values
      • Handling with categorical data
      • Nominal and Ordinal features
      • Encoding class labels
      • One hot encoding
      • Split data into training and testing sets
      • Feature scaling

      Module 3: Machine Learning Classifiers

      • K-Nearest Neighbors (KNN)
      • Decision tree
      • Random forest
      • Support vector machines (SVM)
      • Naive Bayes
      • Logistic Regression
      • Email Spam Filtering Project

      Module 4: Regression Based Learning

      • Simple Regression
      • Multiple Regression
      • Predicting house prices with Regression

      Module 5

      Clustering Based Learning

      • Definition
      • Types of clustering
      • The k-means clustering algorithm

      Module 6

      Natural Language Processing

      • Install nltk
      • Tokenize words
      • Tokenizing sentences
      • Stop words with NLTK
      • Stemming words with NLTK
      • Twitter Sentiment analysis Project

      Module 7

      Working with OpenCV

      • Installing opencv
      • Reading  and writing images
      • Applying image filters
      • Writing text on images
      • Image Manipulations
      • Face detection Project
      • Speech Recognition Project

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    5th Aug
    6 months 3 months Mon-Fri
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    48 sessions NA Sat & Sun
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