DATASCIENCE & MACHINE LEARNING WITH PYTHON

Featured Review

4.7 (2080 Ratings)

Data Science and Machine Learning with Python is a field of study and practice that combines data analysis, statistical modeling, and machine learning techniques using the Python programming language. Data Science and Machine Learning with Python involve the use of Python programming along with statistical analysis and machine learning techniques to extract insights, build predictive models, and solve complex data-related problems across various domains, including finance, healthcare, marketing, and more.

0 +

Google Reviews

0 +

JustDial Reviews

Overview

The Syllabus

Curriculum Designed by Experts

Course content summary -Introduction to Data Science with Python / Python Essentials

Course content summary -Introduction to Data Science with Python

What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project

Python Essentials

Why Python for data science?
Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical – string – date
Reading and writing data
Simple plotting
Control flow & conditional statements
Debugging & Code profiling
How to create class and modules and how to call them?
Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

Accessing/Importing And Exporting Data Using Python Modules / Data Manipulation – Cleansing – Munging using python modules

Accessing/Importing And Exporting Data Using Python Modules

Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects – subsetting Data, methods
Exporting Data to various formats
Important python modules: Pandas, beautiful soup

Data Manipulation – Cleansing – Munging using python modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

Data Analysis – Visualization Using Python / Introduction to Statistics

Data Analysis – Visualization Using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc)

Introduction to Statistics

Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests( One sample, independent, paired), Analysis of variance, Correlations and Chi-square
Important modules for statistical methods: NumPy, SciPy, Pandas

Introduction to Predictive Modelling / Data Exploration For Modelling

Introduction to Predictive Modelling

Concept of model in analytics and how it is used?
Common terminology used in analytics & Modelling process
Popular modelling algorithms
Types of Business problems – Mapping of Techniques
Different Phases of Predictive Modelling

Data Exploration For Modelling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

Data Preparation / Segmentation: Solving Segmentation Problems

Data Preparation

Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

Segmentation: Solving Segmentation Problems

Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioural Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling – Identify cluster characteristics
Interpretation of results – Implementation on new data

Linear Regression: Solving Regression Problems / Logistic Regression : Solving Classification Problems

Linear Regression: Solving Regression Problems

Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression : Solving Classification Problems

Introduction – Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting : Solving Forecasting Problems / Machine Learning

Time Series Forecasting : Solving Forecasting Problems

Introduction – Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based – Pattern less)
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning

Introduction to Machine Learning & Predictive Modelling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Unsupervised Learning : Segmentation / Supervised Learning :- Decision Trees

Unsupervised Learning : Segmentation

What is segmentation & Role of ML in Segmentation?
Concept of Distance and related math background
K-Means Clustering
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principle component Analysis (PCA)

Supervised Learning :- Decision Trees

Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees – Validation
Overfitting – Best Practices to avoid

Supervised Learning :- Ensemble Learning / Supervised Learning :- Artificial Neural Network – ANN

Supervised Learning :- Ensemble Learning

Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Supervised Learning :- Artificial Neural Network – ANN

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Supervised Learning :- Support Vector Machines / Supervised Learning :-KNN

Supervised Learning :- Support Vector Machines

Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models

Supervised Learning :-KNN

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Supervised Learning :- Naive Bayes / Text Mining And Analytics

Supervised Learning :- Naive Bayes

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Text Mining And Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using Python
Text Analytics – Word cloud analysis using Python
Text Analytics – Segmentation using K-Means/Hierarchical Clustering
Text Analytics – Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.

You should be able to demonstrate these skills / Course Contains

You should be able to demonstrate these skills

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Course Contains

Introduction to Data Science with Python

What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project

Outline for this course / Audience for this course

Outline for this course

Introduction to Data Science with Python

Python Essentials

Accessing/Importing And Exporting Data Using Python Modules

Data Manipulation – Cleansing – Munging using python modules

Data Analysis – Visualization Using Python

Introduction to Statistics

Introduction to Predictive Modelling

Data Exploration For Modelling

Data Preparation

Segmentation: Solving Segmentation Problems

Linear Regression: Solving Regression Problems

Logistic Regression : Solving Classification Problems

Unsupervised Learning : Segmentation

Supervised Learning :- Decision Trees

Time Series Forecasting : Solving Forecasting Problems

Machine Learning : Predictive Modelling

Supervised Learning :- Ensemble Learning

Supervised Learning :- Artificial Neural Network – ANN

Supervised Learning :- Support Vector Machines

Supervised Learning :-KNN

Supervised Learning :- Naive Bayes

Text Mining And Analytics

Audience for this course

Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
Engineers who want to use a distributed computing engine for batch or stream processing or both
Analysts who want to leverage Spark for analyzing interesting datasets
Data Scientists who want a single engine for analyzing and modelling data
MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being data scientists
Professionals who’ve worked mostly with tools like Excel and want to learn how to use Python for statistical analysis.

Enquire Now

Why Radical Technologies

Live Online Training

Highly practical oriented training

Installation of Software On your System

24/7 Email and Phone Support

100% Placement Assistance until you get placed

Global Certification Preparation

Trainer Student Interactive Portal

Assignments and Projects Guided by Mentors

And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches

80:20 Practical and Theory Ratio

Real-life Case Studies

Easy Coverup if you missed any sessions

PSI | Kryterion | Redhat Test Centers

Life Time Video Classroom Access ( coming soon )

Resume Preparations and Mock Interviews

And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Self Paced Training

Self Paced Learning

Learn 300+ Courses at Your Own Time

50000+ Satisfied Learners

Course Completion Certificate

Practical Labs Available

Mentor Support Available

Doubt Clearing Session Available

Attend Our Virtual Job Fair

10% Discounted Global Certification

Course completion certificate and Global Certifications are part of our all Master Program

Skills Covered

Programming Languages
Statistical Analysis
Machine Learning Algorithms
Communication and Collaboration

Data Visualization
Data Wrangling and Preprocessing
Feature Selection and Engineering

Model Evaluation and Validation
Deployment and Productionization
Domain Knowledge

tool covered

Like the Curriculum ? Let's Get Started

Why Enroll for Data science & Machine Learning With Python ?

In-Demand Skills

Data science and machine learning are among the fastest-growing fields in technology. Acquiring skills in these areas can significantly enhance your employability and open up diverse career opportunities in industries such as healthcare, finance, e-commerce, and more.

Data-Driven Decision Making

Data science equips you with the tools and techniques to extract valuable insights from large volumes of data. By applying machine learning algorithms, you can uncover patterns, trends, and correlations in data that can inform strategic decision-making and drive business growth.

Predictive Analytics

Machine learning enables predictive modeling, allowing organizations to forecast future outcomes based on historical data. This capability is valuable for tasks such as sales forecasting, demand planning, risk management, and predictive maintenance.

Cross-Disciplinary Applications

Data science and machine learning have applications across various domains, including healthcare, finance, marketing, cybersecurity, manufacturing, and more. Acquiring proficiency in these areas provides you with transferable skills that are applicable across different industries and sectors.

Automation and Efficiency

Machine learning can automate repetitive tasks and streamline processes in various domains, leading to increased efficiency and productivity. By leveraging machine learning algorithms, you can automate data preprocessing, feature engineering, model training, and evaluation, thereby freeing up time for more complex tasks

Advanced Analytics Capabilities

Data science and machine learning enable advanced analytics capabilities, including sentiment analysis, natural language processing (NLP), image recognition, and anomaly detection. These techniques can provide valuable insights and solutions for tasks such as customer sentiment analysis, text classification, image classification, and fraud detection.

Competitive Advantage

Organizations that leverage data science and machine learning effectively gain a competitive edge by leveraging data-driven insights to innovate, optimize processes, and deliver better products and services to customers. By mastering these skills, you can help organizations stay ahead of the competition and drive innovation.

Personalization and Recommendation Systems

Machine learning powers personalized experiences and recommendation systems in various applications, including e-commerce, streaming platforms, social media, and online advertising. By understanding machine learning techniques, you can design and implement effective recommendation algorithms to enhance user engagement and satisfaction.

Career Growth Opportunities

Data scientists and machine learning engineers are in high demand and command competitive salaries. By learning data science with machine learning, you can position yourself for lucrative career opportunities and continuous career growth as these fields continue to evolve and expand.

Course benefits

Comprehensive Skill Development
In-Demand Skills
Real-World Projects
Career Advancement
Flexibility and Accessibility
Networking Opportunities
Continuous Learning
Problem-Solving Skills
Industry-Relevant Curriculum

Who Can Apply for Red Hat Linux

Information Architects and Statisticians
Developers looking to master Machine Learning and Predictive Analytics
Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
Anyone who wants to learn machine learning, artificial intelligence, data visualization, data analytics, data structures, and algorithms (DSA).

Why Data science & Machine Learning With Python ?

Scalability

Unlock the power of scalability in Data Science & Machine Learning with Python Training. Python's versatile libraries like Pandas, NumPy, and Scikit-learn allow data scientists to handle vast datasets efficiently, preparing you to master real-world applications in a scalable manner. Start learning today!

Flexibility

Discover the flexibility of Data Science & Machine Learning with Python Training. Python's vast libraries—like TensorFlow, Pandas, and Matplotlib—adapt to diverse data challenges. Gain hands-on experience with versatile tools, preparing for dynamic real-world applications. Start today!

Security and Compliance

Learn Security and Compliance in Data Science & Machine Learning with Python Training. Python's robust libraries help ensure data privacy and regulatory compliance, vital for handling sensitive datasets. Master secure practices while building powerful machine learning models. Start your course today!

Hybrid Capabilities

Explore Hybrid Capabilities in Data Science & Machine Learning with Python Training. Python’s integration with big data tools and cloud platforms enables seamless hybrid solutions, combining local and distributed computing. Master these capabilities to enhance your data projects. Enroll today!

Cost Effectiveness

Discover the cost-effectiveness of Data Science & Machine Learning with Python Training. Python’s open-source libraries reduce development costs, offering powerful tools like NumPy and Scikit-learn. Learn to build scalable models without expensive software. Start your affordable journey today!

Innovation

Unlock innovation with Data Science & Machine Learning with Python Training. Python's evolving ecosystem fosters creativity through cutting-edge libraries like TensorFlow and Keras. Learn to develop innovative models that push boundaries in AI and data science. Start your journey toward innovation today!

Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering a wide range of globally recognized certifications across various domains. With expert trainers and comprehensive course materials, it ensures that students gain in-depth knowledge and hands-on experience to excel in their careers. The institute’s certification programs are tailored to meet industry standards, helping professionals enhance their skillsets and boost their career prospects. From cloud technologies to data science, Radical Technologies covers it all, empowering individuals to stay ahead in the ever-evolving tech landscape. Achieve your professional goals with certifications that matter.

Online Classroom PREFERRED

Live Classes from IIT Faculty & Industry Experts
Certification from IHUB IIT Roorkee
Career Services (Mock Interviews, Resume Preparation)
Placement Assistance upon clearing PRT
Dedicated Learning Manage

Discount Voucher

"Register Now to Secure Your Spot in Our Featured Course !"

BOOK HERE

career services

Job Assistance
Interview Preparation
Profile Buliding

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalized resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career. Join us and let your journey to a successful future begin with the right support.

At Radical Technologies, we ensure you’re ready to shine in any interview. Our comprehensive Interview Preparation program includes mock interviews, expert feedback, and tailored coaching sessions to build your confidence. Learn how to effectively communicate your skills, handle technical questions, and make a lasting impression on potential employers. With our guidance, you’ll walk into your interviews prepared and poised for success.

At Radical Technologies, we believe that a strong professional profile is key to standing out in the competitive IT industry. Our Profile Building services are designed to highlight your unique skills and experiences, crafting a resume and LinkedIn profile that resonate with employers. From tailored advice on showcasing your strengths to tips on optimizing your online presence, we provide the tools you need to make a lasting impression. Let us help you build a profile that opens doors to your dream career.

Red Hat Linux Course Projects

Infrastructure Provisioning

And Configuration Management

Implementing automated infrastructure provisioning and configuration management using Ansible. This may include setting up servers, networking devices, and other infrastructure components using playbooks and roles.

Applications Deployment

And Orchestration

Automating the deployment and orchestration of applications across development, testing, and production environments. This could involve deploying web servers, databases. middleware, and other application components using Ansible

Continuous Integration

And Continuous Deployment

Integrating Ansible into CI/CD pipelines to automate software. build, test, and deployment processes. This may include automating the creation of build artifacts, running tests, and deploying applications to various environments.

Radical Learning Eco-System

Exam simulator

Cloud Send Borey

Hands - on Cloud Lab

Developer Coding Ground

Testimonials

Play

(DATA SCIENCE)Antrixsh teaches us lots of things in easy way and very understanding. Radical Technologies is systematic planed institute in pune.

Satisfied Student

Antrixsh is best trainer for data science. He has very practical approach. Radical Technology is best institute for BigData and DataScience professional course in Pune.

Satisfied Student

Antrixsh is really a great tutor when it comes to Data Science teaching. Being from a different platform, i have learned a lot from him and the time and money is really worth when it comes to learning Data Science from Antrixsh Sir. …

Satisfied Student

Attended Data Science batch in Radical aundh and must say that Antrixsh is the best trainer/ mentor I have ever met. He made things simple but beyond that he provided us with many scenarios to work on. One should not expect to become a …

Satisfied Student

Best Data Science class in entire Pune. Thanks to Antrixsh sir for the excellent teaching.

Satisfied Student

Course content for Data Science was good but forming a batch takes time.

Satisfied Student

Good institutions and trainer for Data science course. Special thanks to Antrixsh

Satisfied Student

Good learning experience at Radical Technologis, Aundh. Thanks to Antrixsh sir for Data Science. He is having good knowledge of content with real time examples explanation which is really helpful to understand.

Satisfied Student

Got good knowledge of hadoop and data science. Thanks for helping us understand the course in very interactive and easy way.

Satisfied Student

I did Big data and Data Science program here with Antriksh sir. All materials is provided like setup documents, ppts, dataset, assignments. It is helpful in switching career to big data. But yes need to spend time to get expert in it

Satisfied Student

I did big data and data science program here with Antrixsh sir. It was really helpful to start your career in this field. All materials and guidance was good. But yes finally if you have to be proficient you needs practice over that...... 😊 …

Satisfied Student

I did the data science course with Radical. The course covered some topics of Bigdata and few topics on data science. This course gives you the basic foundations required for starting your journey in the data science field.

Satisfied Student

I had a very nice experience with the course Data-Science at Radical Technologies - Aundh. Antrixsh is a very good trainer and presents things in a very lucid and …

Satisfied Student

I had enrolled for data science and machine learning course conducted by instructor Antrixsh Gupta. He's a great teacher and always available to answer the doubts. I was really a novice in this field but with the guidance of Antrixsh sir I was able to make a career transition into this field.

Satisfied Student

I had pursued Big_Data-Data Science course from Radical Tech, Aundh, Pune for Jan 2018 weekend batch. It was indeed a great learning. It is good that Radical Tech offers Data Science training program comprising most of the modules at …

Satisfied Student

I have completed the training for Data Science under the guidance of Antrixsh Gupta sir.All my queries are answered and the best thing about the class is that, real world examples are discussed and that helps us to relate how data science works in real world problem.

Satisfied Student

I just completed my Data Science training, my mentor was Antrixsh Gupta there, he is a very patient and knowledgeable tutor, He came extremely prepared which impressed me, He was always available to answer any questions we had, apart from the class, many times he took us to Lunch and Dinner after the classes, would recommend to anyone.

Satisfied Student

It was a good learning experience learning data science mentored by Antarixsh Gupta sir. Practical examples along with detailed explanation are taught in class which make concepts even clearer.

Satisfied Student

It was great experience in learning new things with "Antarixsh Gupta" Sir. His knowledge on Data Science and guidance has changed our thoughts and gave us opportunity to learn new things. Well I was fortunate enough to start my career as …

Satisfied Student

It was very good experience with Radical. I have done Data Science course under the guidance of Antrixsh Gupta. Antrixsh Sir is technically one the best coach i have meet till date. …

Satisfied Student

Trainer Antrixsh is very good for data science. He has very practical approach. Needless to say Radicals is best institute for any professional courses in Pune.

Satisfied Student

Attended training on Data Science with R & python in radical technologies Hinjewadi and had a really good experience. The trainers here have sound knowledge on the subjects and keep themselves updated with the latest trends in the market. …

Satisfied Student

I can say that it's one of the best Insistute to learn phython, datascience, machine learning. I have completed phython with datascience in radical technologies hinjewadi. In simple word I will say he is just perfect trainer for phython …

Satisfied Student

I have attended datascience and Google cloud from radical technologies Hinjewadi,trainers are very knowledgeable ,even you are not in to IT.they covers topics in such a ease that everyone take well.The infrastructure also good.

Satisfied Student

I have joined radical technologies hinjewadi for datascience with python +machine learning and R. Trainer explained all the concepts clearly. The training was completely based on real time scenario and they gave one life project also . I …

Satisfied Student

Our Alumni

Data science & Machine Learning With Python FAQs

Why should I choose Radical Technologies for Data Science Training in Pune?

Radical Technologies stands out for its comprehensive Data Science Course in Pune. With expert instructors, hands-on projects, and a focus on real-world applications, our training is designed to empower aspiring data scientists.

Can beginners enroll in the Data Science Course, or is it suitable for experienced professionals only?

Absolutely! Our Data Science Course in Pune caters to all levels of expertise, including beginners. We provide a structured curriculum that starts with fundamentals and progresses to advanced topics, ensuring a comfortable learning experience for everyone.

What sets apart Radical Technologies' Data Science Classes in Pune?

Is the Data Science Certification provided by Radical Technologies recognized in the industry?

Yes, the Data Science Certification awarded upon completion of our course is recognized in the industry. It adds value to your resume and enhances your credibility as a skilled data science professional.

How does Radical Technologies support online learners with its Data Science Online Training in Pune?

Our Online Data Science Training in Pune is designed for remote learners, offering flexibility without compromising on the quality of education. Live sessions, interactive discussions, and hands-on projects ensure an engaging online learning experience.

Does Radical Technologies offer job placement assistance after completing the Data Science Course?

Yes, we provide Job Placement Assistance to help you transition smoothly into the industry. We also offer a Job Placement Guarantee for the right candidates who successfully complete our Data Science Course in Pune.

Can I pursue a Python Data Science Course separately at Radical Technologies?

Certainly! Our Python Data Science Course is integrated into the Data Science curriculum, but we also offer it as a standalone course for those specifically interested in mastering Python for data science applications.

What is the duration and fees structure of the Data Science Course at Radical Technologies?

For detailed information on the Data Science Course Duration and Fees in Pune, please visit our website or contact our admissions team for personalized assistance.

Are there specialized courses for Data Science and AI at Radical Technologies?

Yes, we offer a specialized course that explores the convergence of Data Science and AI. This advanced program is designed for those seeking expertise at the intersection of these two cutting-edge fields.

How can working professionals benefit from the Data Science Courses at Radical Technologies?

Our Data Science Courses are designed to accommodate the schedules of working professionals. With flexible timings and online options, our courses provide a convenient way for working individuals to upskill and advance their careers in data science.

Data science & Machine Learning With Python Course in Other Cities

Online Batches Available for the Areas

Most Probable Interview Questions Asked for Data Science & Machine Learning with Python

Can you explain the difference between supervised and unsupervised learning, and provide examples of each?
Describe your experience with data preprocessing techniques in Python, including handling missing values, scaling features, and encoding categorical variables.
How do you evaluate the performance of a machine learning model in Python, and what evaluation metrics do you consider for classification and regression tasks?
Can you discuss the advantages and disadvantages of different machine learning algorithms such as decision trees, random forests, and support vector machines?
Describe your approach to feature selection and feature engineering in Python, and how you identify and create relevant features for improving model performance.

Discuss your experience with cross-validation techniques such as k-fold cross-validation and how you use them to assess model generalization and avoid overfitting.
Can you explain the concept of bias-variance tradeoff in machine learning, and how you address it when training and evaluating models in Python?
Describe your experience with ensemble learning methods such as bagging, boosting, and stacking, and how you combine multiple models to improve predictive performance.
How do you handle imbalanced datasets in Python, and what strategies do you use to address class imbalance in classification tasks?
Can you discuss your approach to hyperparameter tuning in Python, including techniques such as grid search, random search, and Bayesian optimization?

Like the Curriculum ? Let's Get Started

Your Seniors Got Placed

Testimonial #1 Designation

I had an amazing experience with this service. The team was incredibly supportive and attentive to my needs. The quality of the work exceeded my expectations. I would highly recommend this to anyone looking for reliable and professional service."

Testimonial #1 Designation

About Data science & Machine Learning With Python Certification

Data Science and Machine Learning have become crucial components in the tech industry, driving innovation and transforming how businesses operate. A certification in Data Science & Machine Learning with Python not only validates your expertise in these fields but also equips you with the skills needed to tackle real-world problems using Python—a powerful and versatile programming language known for its simplicity and vast library support.

Course Overview

This certification program is meticulously designed to cater to both beginners and professionals seeking to deepen their understanding of Data Science and Machine Learning. The course covers a wide array of topics, ensuring a holistic learning experience:

Python Programming Fundamentals: Begin your journey by mastering Python, the backbone of data science. Learn about variables, data structures, control flows, functions, and more, laying a strong foundation for your data science endeavors.
Data Analysis and Visualization: Dive into the world of data by exploring libraries like Pandas, NumPy, and Matplotlib. Gain hands-on experience in data manipulation, cleaning, and visualization techniques, crucial for deriving insights from raw data.
Statistical Analysis: Understand the core concepts of statistics that underpin data science. Topics like probability distributions, hypothesis testing, and regression analysis will be covered to enhance your analytical capabilities.
Machine Learning Algorithms: The course delves into various machine learning algorithms, including supervised and unsupervised learning. Explore linear regression, decision trees, support vector machines, k-means clustering, and more, understanding how to implement them in Python.
Deep Learning Basics: Get introduced to the fascinating world of deep learning. Learn the basics of neural networks, and how to build and train them using popular frameworks like TensorFlow and Keras.
Real-World Applications: The program emphasizes practical applications, where you will work on real-world projects that involve predicting trends, classifying data, and making data-driven decisions. This hands-on approach ensures that you can apply what you learn directly to your career.
Model Evaluation and Optimization: Learn how to evaluate the performance of your models and optimize them for better accuracy. Techniques such as cross-validation, hyperparameter tuning, and model selection will be discussed in detail.

Key Benefits of the Certification

Industry-Relevant Skills: The course content is regularly updated to reflect the latest trends and demands in the industry, ensuring that you acquire skills that are immediately applicable in your job.
Practical Experience: With a focus on hands-on projects and assignments, this certification enables you to build a robust portfolio showcasing your expertise to potential employers.
Expert Instructors: Learn from industry experts who bring their vast experience into the classroom, offering insights that go beyond textbook knowledge.
Flexible Learning: The certification is designed to be flexible, allowing you to learn at your own pace. Whether you prefer self-paced learning or instructor-led sessions, there is a mode that fits your schedule.
Career Advancement: With the growing demand for data scientists and machine learning engineers, this certification can significantly boost your career prospects, opening doors to new opportunities and roles.

Who Should Enroll?

This certification is ideal for:

Aspiring Data Scientists and Machine Learning Engineers: Individuals looking to break into the field of data science and machine learning.
Professionals Seeking Upskilling: IT professionals, software engineers, and data analysts aiming to enhance their skill set and stay competitive in the job market.
Students and Graduates: Those who are pursuing or have completed a degree in computer science, engineering, mathematics, or related fields and wish to specialize in data science and machine learning.

Certification Process

To earn the Data Science & Machine Learning with Python Certification, participants must:

Complete the Course Modules: Engage with the learning materials, complete quizzes, and participate in discussions.
Work on Capstone Projects: Apply your knowledge to real-world problems by completing capstone projects that will be evaluated by your instructors.
Pass the Final Examination: Demonstrate your understanding of the course content through a comprehensive exam.

Applications of Data science & Machine Learning With Python

Data Science and Machine Learning have become integral to various industries, driving innovation and efficiency. Their applications are diverse, ranging from enhancing business processes to developing cutting-edge technologies that shape our daily lives. Here’s a detailed look at some of the most impactful applications:

1. Healthcare

Predictive Analytics: Data Science and Machine Learning are transforming healthcare by enabling predictive analytics for patient outcomes. By analyzing historical data, models can predict disease progression, patient readmissions, and treatment responses.
Personalized Medicine: Machine Learning algorithms help in creating personalized treatment plans based on individual patient data, including genetics, lifestyle, and medical history, leading to more effective healthcare solutions.
Medical Imaging: Advanced image recognition techniques are used to analyze medical images, such as X-rays and MRIs, for detecting anomalies, such as tumors or fractures, with higher accuracy and speed.

2. Finance

Fraud Detection: Machine Learning models are employed to detect fraudulent transactions by analyzing patterns in financial data. These models can identify unusual activities in real-time, reducing the risk of fraud.
Algorithmic Trading: Data Science is the backbone of algorithmic trading, where trading decisions are made based on the analysis of vast datasets. Machine Learning models can predict stock price movements and execute trades at optimal times.
Credit Scoring: Financial institutions use Machine Learning to assess the creditworthiness of individuals and businesses. By analyzing various factors such as spending habits, payment history, and social behavior, more accurate credit scores are generated.

3. Retail

Customer Segmentation: Retailers use Data Science to segment customers based on purchasing behavior, demographics, and preferences. This allows for targeted marketing campaigns, personalized offers, and improved customer satisfaction.
Inventory Management: Machine Learning models predict demand for products, helping retailers optimize inventory levels, reduce stockouts, and minimize waste.
Recommendation Systems: E-commerce platforms utilize recommendation engines powered by Machine Learning to suggest products to customers based on their browsing history, past purchases, and preferences, driving sales and enhancing user experience.

4. Manufacturing

Predictive Maintenance: In manufacturing, Data Science is used to predict equipment failures before they happen. By analyzing sensor data and historical maintenance records, Machine Learning models can forecast when machines are likely to fail, allowing for timely maintenance and reducing downtime.
Quality Control: Machine Learning algorithms are employed in quality control processes to detect defects in products. Automated systems can analyze images or sensor data to identify imperfections that may be missed by human inspectors.
Supply Chain Optimization: Data Science helps in optimizing supply chain operations by predicting demand, identifying bottlenecks, and improving logistics, leading to cost savings and enhanced efficiency.

5. Transportation

Autonomous Vehicles: The development of self-driving cars heavily relies on Data Science and Machine Learning. These technologies enable vehicles to understand and respond to their environment by processing data from sensors, cameras, and GPS systems.
Route Optimization: Transportation companies use Machine Learning to optimize delivery routes, reducing travel time and fuel consumption. This is especially valuable in logistics and public transportation systems.
Traffic Management: Machine Learning models analyze traffic patterns to predict congestion and optimize traffic light timings, contributing to smoother traffic flow and reduced emissions.

6. Marketing

Customer Behavior Analysis: Data Science is crucial in understanding customer behavior by analyzing data from various sources such as social media, website analytics, and transaction records. This insight allows for more effective marketing strategies.
Sentiment Analysis: Machine Learning techniques are used to analyze customer sentiment from reviews, comments, and social media posts. Businesses can gauge public opinion and adjust their strategies accordingly.
Ad Targeting: Machine Learning algorithms are used to deliver personalized ads to users based on their interests, browsing history, and demographic data, increasing the effectiveness of marketing campaigns.

7. Energy

Energy Consumption Forecasting: Data Science models predict energy consumption patterns, enabling utilities to optimize energy production and reduce waste. This is particularly important for managing renewable energy sources like wind and solar.
Smart Grid Management: Machine Learning is used in managing smart grids by analyzing data from various sensors across the grid. This helps in detecting faults, optimizing energy distribution, and preventing outages.
Renewable Energy Optimization: Data Science assists in optimizing the operation of renewable energy sources by predicting weather conditions, energy generation potential, and storage needs.

8. Entertainment

Content Recommendation: Streaming services like Netflix and Spotify use Machine Learning algorithms to recommend movies, shows, and music to users based on their preferences and behavior.
Content Creation: Data Science is being explored in generating content, such as writing scripts or composing music, by analyzing existing works and understanding patterns in creative expression.
Audience Analytics: Entertainment companies analyze viewer data to understand audience preferences and optimize content production, marketing, and distribution strategies.

9. Education

Personalized Learning: Machine Learning models are used to create personalized learning experiences by analyzing student performance and adapting content to their needs. This helps in improving learning outcomes.
Administrative Efficiency: Data Science is employed in managing various administrative tasks such as enrollment, scheduling, and resource allocation, making educational institutions more efficient.
Student Performance Prediction: Educators use Machine Learning to predict student performance and identify those at risk of falling behind, enabling timely interventions and support.

10. Natural Language Processing (NLP)

Language Translation: Machine Learning models power translation services like Google Translate, enabling real-time translation between multiple languages.
Chatbots and Virtual Assistants: NLP applications in chatbots and virtual assistants like Siri and Alexa provide users with intelligent, context-aware responses, enhancing user interaction.
Sentiment Analysis: NLP techniques are widely used in analyzing text data to understand sentiment, which is valuable for businesses in understanding customer feedback and public opinion.

Data science & Machine Learning With Python Course Certification With Training In Pune

Unlock the world of data with Radical Technologies’ comprehensive Data Science Course in Pune. Our Data Science Classes cover the spectrum, providing hands-on training for aspiring data scientists. Whether you’re a beginner or a seasoned professional, our Data Scientist Course in Pune caters to all levels of expertise.