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Data Science with Python Training Course in Bangalore India

Data Science Training Course in Bangalore Live Classes

  (175 Reviews)
      700 Registered Users

We provide an advanced level training experience for candidates that combine real-time projects and practical approach. Get certified from leading data science with the python training program in Bangalore.

Data Science Course in Bangalore
42,000 + GST
41,999 + GST

(0% Interest on EMI)

COURSE DESCRIPTION

NearLearn is a leading and top-rate Data Science with a Python training institute in Bangalore. We hold an extensive curriculum that provides the best and advanced learning experience for major technical data science concepts with real-time projects. The candidates who received certification from top data science with python training Bangalore will get more value and confidence in getting a job in the desired field.

We are specialized in providing advance level data science with python training in Bangalore with exact concepts offered by our industry experts. We hold the staff who are well-known and holding a technical background with more than 10+ years of experience in providing data science with python training in Bangalore.

Our trainers create job faced Data Science with Python training. We provide education on a real-time plan which supports learners in a more reliable understanding. Our trainers also encourage the student to get a position in the best MNCs by training at various crafts. We have composed Data Science with Python course and syllabus to reach their expert purposes.

  • NearLearn holding 4+ years of experience in providing training in all software course
  • All our courses are designed with the advanced level that matches your current criteria
  • We provide all the latest strategies to become confidence after completing courses
  • Training will be conducted on a daily and weekly basis with flexible timing as per candidate preference.
  • Live Project based practice with instructors holding more than 10 years of Industry Expertise.
  • Education will be accompanied by certified experts.
  • Our Labs are extremely well-equipped with the most advanced version of the device and software.
  • Our classrooms are completely blended up with projectors &Wi-Fi passage.
  • You will receive study stuff in the style of E-Books, Videos and a bunch of interview questions and answers including project material.
  • Adaptable Pay options such as Net Banking, EMI, Cash, Card, Debit Card, Cheque, and Credit
  • Trainers have accredited experts with more than 10+ years of expertise in their particular field as well as they are currently managing with Top companies
  • As all Instructors are operating experts so they are holding many live plans, instructors will accept these projects during practice sessions.
  • Introduction with analytics and data science
  • Learning common Terms and basics about Analytics
  • Comparison Analytics vs OLAP and MIS Reporting
  • Discussion about problems of business objectives in various enterprises
  • How does Data Science work in Various Industries?
  • Important progress operators
  • Summary of analytics tools & their reputation
  • Analytics Methodology & problem-solving structure
  • Outline of actions in Analytics projects
  • Know the most relevant solution plan for the given query record
  • Why data science with Python?
  • Intro to the connection of Python
  • Intro to Python Editors & pycharm, Jupyter, Rodeo, Ipython
  • Comprehend Jupyter record & Customize Frames
  • Theory of Sets/Libraries - Significant combinations(Matplotlib,sci-kit learn,NumPy, SciPy, Pandas, etc)
  • Connecting & storing Combinations & Title Spaces
  • Data sorts & Data targets/buildings (series, Tuples, Dictionaries)
  • Basic Methods - Analytics - order - date
  • Learning and composing data
  • Easy plotting
  • Power flow & qualified records
  • Debugging & Code extrapolating data
  • How to design class and elements?
  • Fundamentals on Python
  • Conceptualization on Data Science such as Data exploration/pre-processing/ Mugging etc
  • Usage of Python in ML

    • COURSES CURRICULUM

    Lecture1.1 Key Elements of Machine Learning & Data Science & differences between them

    Lecture1.2 Data Warehousing

    Lecture1.3 Business Intelligence

    Lecture1.4 Data Visualization

    Lecture1.5 Data Mining

    Lecture1.6 Machine Learning

    Lecture1.7 Artificial Intelligence

    Lecture1.8 Cloud Computing

    Lecture1.9 Big Data

    Lecture2.1 What is Machine Learning (ML)?

    Lecture2.2 How machines learn

    Lecture2.3 Basics of Classification, Regression and Clustering algorithms

    Lecture2.4 Creating your first Prediction Model

    Lecture2.5 Training & Model Evaluation

    Lecture2.6 Choosing Machine Learning Algorithm

    Lecture3.1 Operators, Operands and Expressions

    Lecture3.2 Python Data Types

    Lecture3.3 Conditional statements in Python

    Lecture3.4 Loops in Python

    Lecture3.5 Lists and dictionaries and Tuples

    Lecture3.6 Programming practice in Python

    Lecture3.7 Iterators & Generators

    Lecture3.8 File Handling in Python

    Lecture3.9 Modules and Libraries

    Lecture3.10 Classes and Objects

    Lecture3.11 String Formatting in Python

    Lecture3.12 Decorators, Context Managers, Regular Expressions

    Lecture3.13 List and Dictionary Comprehensions

    Lecture3.14 Lambda and Argument Passing

    Lecture3.15 Multiple Inheritance

    Lecture4.1 Linear Algebra (Vectors, Matrix, Eigen Values)

    Lecture4.2 Probability and Statistics

    Lecture4.3 Hypothesis testing

    Lecture4.4 Optimization

    Lecture5.1 Introduction to Numpy

    Lecture5.2 Arrays, Matrices,

    Lecture5.3 Various operations on arrays and matrices

    Lecture5.4 Introduction to Pandas

    Lecture5.5 Reading csv and matlab files

    Lecture5.6 Data frame object manipulation in python

    Lecture5.7 Various operations on data frame

    Lecture5.8 Visualization using Matplotlib

    Lecture5.9 Scatter plots, line plots etc on a given data

    Lecture5.10 Advance visualization using Seaborn

    Lecture5.11 Histograms, heatmaps, box plots etc using seaborn

    Lecture6.1 Basic Functionalities of a data object

    Lecture6.2 Merging of Data objects

    Lecture6.3 Concatenation of data objects

    Lecture6.4 Types of Joins on data objects

    Lecture6.5 Exploring a Dataset

    Lecture6.6 Analysing a dataset

    Lecture6.7 Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()

    Lecture6.8 GroupBy operations

    Lecture6.9 Aggregation

    Lecture6.10 Concatenation

    Lecture6.11 Merging

    Lecture6.12 Joining

    Lecture6.13 Data Collection &Preparation

    Lecture6.14 Data Mugging

    Lecture6.15 Outlier Analysis

    Lecture6.16 Missing value treatment

    Lecture6.17 Feature Engineering

    Lecture6.18 Data Transformation

    Lecture6.19 Normalization vs Standardization

    Lecture6.20 Creating Dummies

    Lecture6.21 Dimensionality Reduction

    Lecture6.22 Principal ComponentAnalysis

    Lecture7.1 Confidence Interval

    Lecture7.2 Student’s t distribution

    Lecture7.3 Binomial Distribution

    Lecture7.4 A/B Testing

    Lecture7.5 Hypothesis Testing

    Lecture7.6 t-Tests

    Lecture7.7 ANOVA

    Lecture7.8 Chi-square test

    Lecture7.9 KNN

    Lecture7.10 PCA

    Lecture7.11 Categorical Variables

    Lecture7.12 R Square

    Lecture8.1 Linear & Logistic and Regression Techniques

    Lecture8.2 Problem of Collinearity

    Lecture8.3 WOE and IV

    Lecture8.4 Residual Analysis

    Lecture8.5 Heteroscedasticity

    Lecture8.6 Homoscedasticity

    Lecture9.1 Supervised Machine Learning algorithms

    Lecture9.2 Linear Regression

    Lecture9.3 Multi Feature

    Lecture9.4 Logistic Regression

    Lecture9.5 2 Class and Multi class

    Lecture9.6 Decision/ Classification

    Lecture9.7 Trees

    Lecture9.8 Ensemble

    Lecture9.9 Models

    Lecture9.10 Bagging

    Lecture9.11 Boosting

    Lecture9.12 Random Forest

    Lecture9.13 K-Nearest Neighbours (KNN)

    Lecture9.14 Naive Bayes

    Lecture9.15 Introduction to Neural Network (DeepLearning)

    Lecture9.16 Feed Forward Neural Network

    Lecture9.17 Forward Propagation

    Lecture9.18 Backward Propagation

    Lecture9.19 Support Vector Machine

    Lecture9.20 Unsupervised Machine Learning algorithms

    Lecture9.21 Clustering with K-means Clustering

    Lecture9.22 Bias-Variance Tradeof

    Lecture9.23 Regularization

    Lecture9.24 Parameter tuning & grid search optimization

    5000+ Handwritten Digit Recognition Problem

    4000+ email spam detection problem

    Image compression Problem

    Flower species classification problem

    Titanic Survivor classification problem from kaggle

    Fifa ranking dataset from kaggle

    Profit Prediction Problem

    Business Case of whether a chip will be accepted or not

    Business case of clustering from dataset

    Wine classification dataset and problem from Kaggle

    Variety of Problems from Kaggle Competition Data Sets

    Reviews

    studant

    Shubham

      (2 Review)

    I have enrolled for NearLearn Machine Learning with Python Course Training. The Session was very Nice. The trainer is knowledgeable and his skills of explaining the concepts are awesome.

    studant

    Sneha Deshpande

      (2 Review)

    I am satisfied with the Machine Learning workshop coducted by NearLearn. The instructor explained course objectives clearly. Made class enjoyable as well as educational.

    studant

    Rudranee

      (2 Review)

    Machine Learning Training session was good.I am satisfied about this course.The training experience will be useful in my work. Course explanation was very clear and understandable.

    studant

    Ketan

      (2 Review)

    The session was very enjoyable as well as educational. This training experience will be very useful in my work. I would really recommend this instructor. Thanks

    FAQs

    Data science is the mining of information by penetrating inside data, market trends, and interpretations using the latest technologies. Data science is a vast sector that includes several scientific technologies and tactics, such as data engineering, hacking mindset, the expertise of various domains, mathematics, statistics, advanced computing, and data visualization. Data scientists are enrolled by various businesses to deeply understand the market trends, predict it, and use it for business development.

    Today’s world is completely digitized where businesses are making huge room for data storage and use. When it comes to making decisions based on available data and predictions based on it, businesses seek a data scientist who can make accurate calculations and help them with the prompt decision. Hence, business industries are offering several job opportunities for certified professionals. Those who are willing to build an exponentially developing career with an excellent pay scale must opt for top Data science Training in Australia and become a data scientist.

    This course is specially designed for the aspirants who are willing to take remote classes being far from the institute. Hence, NearLearn offers an online training session for most of the curriculum and classroom classes for those sessions which requires an expert’s assistance to perform; this especially includes real-time project assessments.

    Most of the students struggle with the study material as they hardly get a described one anywhere. Grabbing every sentence from the lecture is not possible every time and we clearly understand that. Hence, we provide a properly structured study material compiled by the experts of the industry with comprehensive information shared with you during your sessions. Another benefit of taking data science course from NearLearn is that we have our every session recorded, so if you miss any of your online session then you can go through the recordings any time you are available. We have structured our training course for your comfort with intentions to educate you with every single detail of data science and develop your skill as a professional data scientist.

    Every aspirant learns new skills to secure a new job with an excellent pay scale. We assist each aspirant to attain a job based on your interests and skills. Our assistance starts right from building a resume, preparation of predictive interview questions, and personality development sessions to face the interview confidently.

    We offer demo sessions of two days for every course which helps aspirants to understand the training structure, adjust your schedule as per the session also know the education method of the trainer before they join NearLearn.

    Yes, there are discounts on group joining for data science course based on the number of aspirants joining and the type of classes they require. To avail of group discounts and to know more about it you need to get in touch with our course counselor.

    Once you clear the certification, then forever you will be a certified data scientist.

    Register Machine Learning Course in BTM
    42,000 + GST
    41,999 + GST

    Upcoming Online Trainings

    April 10th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

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