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Data Science Classroom Training
Elevate Your Data Science Game with NearLearn
Data science is an ever-evolving field, and with NearLearn, you can stay ahead of the game. Our comprehensive data science course in Bangalore is designed to help you unlock your potential and become an expert in this field.
Earn certificates you complete courses covering the whole data science work process. Regardless of whether you're simply beginning or a prepared expert, you'll improve your aptitudes in data manipulation, data visualization, statistics, AI, and more Quicken your profession with the selective Data Scientist Master's Program as a team with Nearlearn. Experience world-class preparation by an industry leader on the most sought after Data Science and Machine learning aptitudes. Gain hands-on exposure to key advancements including R, SAS, Python, Tableau, Hadoop, and Spark. Become a specialist Data Scientist today.
With Nearlearn introduces understudies with incorporated blended learning, making them specialists in Artificial Intelligence and Data Science. Endless supply of this current Master's Program, Participant will receive the certificate from Nearlearn in the Data Scientist courses on the successful learning path. These certificates will vouch for your aptitudes as a specialist in Data Science. You will likewise get the accompanying:
Placements in Top IT MNC companies
Industry-perceived Master's Certificate from NearLearn
Data Scientist is probably the hottest profession. Nearlearn predicts the interest for Data Scientists will ascend by 28% by 2020. Nearlearn’s Data Scientist Master's Program urges you to master abilities including statistics, hypothesis testing, Data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, suggestion motor, administered, and unaided learning and then some.
This Data Scientist Master's Program covers broad Data Science training, joining Classroom educator drove classes and self-paced learning co-created with Nearlearn. The program finishes up with a capstone project intended to strengthen the learning by building a genuine industry product enveloping all the key angles learned all through the program. The abilities concentrated on right now help set you up for the job of a Data Scientist.
The Data Science job requires an amalgam of experience, data science information, and the right apparatuses and technologies. It is a strong career decision for both new and experienced experts. Aspiring experts of any instructive foundation with a logical mood are generally fit to seek after the Data Scientist Master's Program, including:
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
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.
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
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.
I am satisfied with the Machine Learning workshop coducted by NearLearn. The instructor explained course objectives clearly. Made class enjoyable as well as educational.
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.
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
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.
I am satisfied with the Machine Learning workshop coducted by NearLearn. The instructor explained course objectives clearly. Made class enjoyable as well as educational.
FAQs
Our course includes case studies and projects that simulate real-world challenges. You will work on projects that will help you apply the concepts you learn during the course.
No prior coding knowledge is required. We start from the basics and gradually move towards advanced concepts, making it easy for you to understand.
You will acquire the skills and knowledge required to become an expert in data science. You will have hands-on experience working on real-world challenges, making you stand out in the job market.
Yes, you will have lifetime access to our course materials. You can revisit the materials to brush up on your skills.
Yes, you will receive a certificate upon completion. We are well-known, especially for our data science Training Course Certification in Bangalore & our certificate is recognized by industry experts and will help boost your career in data science.
Yes, we provide job placement assistance and interview preparation to all our students. We will help you prepare your resume and provide tips for your job interview.
Upcoming Classroom Trainings
October 1st | Weekdays | 8 Weeks | 09:30AM TO 10:30AM
October 12th | Weekends | 12 Weeks | 9:00AM TO 12PM
October 14th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM
October 26th | Weekends | 12 Weeks | 10:00AM TO 01PM
November 5th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM
November 9th | Weekends | 12 Weeks | 9:00AM TO 12PM
November 19th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM
November 23rd | Weekends | 12 Weeks | 10:00AM TO 01PM
December 3rd | Weekdays | 8 Weeks | 09:30AM TO 10:30AM
December 14th | Weekends | 12 Weeks | 9:00AM TO 12PM
December 17th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM
December 28th | Weekends | 12 Weeks | 10:00AM TO 01PM