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DATASCIENCE & MACHINE LEARNING WITH PYTHON
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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.
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.
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Why Radical Technologies
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Trainer Student Interactive Portal
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Course completion certificate and Global Certifications are part of our all Master Program
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And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program
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Learn 300+ Courses at Your Own Time
50000+ Satisfied Learners
Course Completion Certificate
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Attend Our Virtual Job Fair
10% Discounted Global Certification
Course completion certificate and Global Certifications are part of our all Master Program
Skills Covered
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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
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
Red Hat Linux Fees in Bangalore
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- Certification from IHUB IIT Roorkee
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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
career services
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.
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Data science & Machine Learning With Python FAQs
What makes Radical Technologies the best choice for a Data Science Course in Bangalore?
At Radical Technologies, we offer a comprehensive Data Science Course in Bangalore that covers everything from basic to advanced concepts. Our expert instructors, practical hands-on training, and industry-aligned curriculum make us the top choice for anyone looking to excel in data science.
What is included in the Machine Learning Course at Radical Technologies?
Our Machine Learning Course includes a thorough curriculum covering all essential aspects of machine learning. You’ll gain expertise in Machine Learning with Python, learn through real-world projects, and receive a certification upon completion. We also offer both in-person and online classes to suit your schedule.
Are the Data Science Classes in Bangalore at Radical Technologies suitable for beginners?
Yes, our Data Science Classes in Bangalore are designed for both beginners and professionals. We provide foundational training, gradually progressing to more advanced topics, ensuring that everyone, regardless of experience level, can benefit from our courses.
What certification do I receive after completing the Machine Learning Course?
Upon successful completion of the Machine Learning Course at Radical Technologies, you will receive a Machine Learning certification. This credential is highly regarded in the industry and can significantly enhance your career prospects.
How flexible are the Machine Learning Online Courses offered by Radical Technologies?
Our Machine Learning Online Courses are designed for flexibility, allowing you to learn at your own pace. They include interactive sessions, recorded lectures, and practical projects, all culminating in a certificate upon completion.
What are the Data Science course fees in Bangalore at Radical Technologies?
The Data Science course fees in Bangalore at Radical Technologies are competitively priced, offering great value for the comprehensive training and certification you receive. For detailed fee information, we encourage you to contact us directly.
Is there a Python Machine Learning course available at Radical Technologies?
Yes, we offer a Python Machine Learning Course that focuses on the application of Python in machine learning. This course is part of our broader Machine Learning Training in Bangalore and is perfect for those looking to specialize in Python-based machine learning.
How do I choose between in-person and online Machine Learning classes?
Choosing between in-person and online Machine Learning classes depends on your learning style and schedule. In-person classes offer direct interaction with instructors, while online courses provide flexibility. Both formats are comprehensive and lead to certification.
What is the syllabus of the Machine Learning Course at Radical Technologies?
The Machine Learning course syllabus at Radical Technologies covers all key areas, including algorithms, data processing, model training, and deployment. The curriculum is regularly updated to include the latest industry trends and practices.
How does Radical Technologies ensure that their Data Science training in Bangalore is industry-relevant?
Our Data Science training in Bangalore is designed in collaboration with industry experts to ensure that it meets current market demands. We focus on practical skills, real-world applications, and up-to-date technologies, preparing you for success in the field.
What kind of support do students receive during the Data Science and Machine Learning course?
Students enrolled in the Data Science and Machine Learning course at Radical Technologies receive continuous support, including access to instructors, additional resources, and career guidance. We are committed to helping you succeed.
How does the Data Science with Python training at Radical Technologies differ from other courses?
Our Data Science with Python training in Bangalore is distinguished by its focus on real-world applications, hands-on projects, and expert instruction. This course is designed to provide you with practical skills that are directly applicable in the workplace.
Can I switch from an in-person Data Science course to an online course if my circumstances change?
Yes, Radical Technologies allows flexibility in switching between in-person and online formats for the Data Science course in Bangalore. We understand that circumstances can change, and we strive to accommodate our students’ needs.
What are the career prospects after completing the Data Scientist course in Bangalore at Radical Technologies?
Completing the Data Scientist Course in Bangalore at Radical Technologies opens up numerous career opportunities in various industries. Our graduates are well-equipped to take on roles such as Data Scientist, Machine Learning Engineer, and more, with many going on to work for top companies.
Does Radical Technologies offer any support in job placement after completing the course?
Yes, Radical Technologies offers job placement support for students who complete our Data Science and Machine Learning certification programs. We have a network of industry partners and provide career counseling to help you secure a position in the field.
Online Batches Available for the Areas
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Most Probable Interview Questions Asked for Data Science & Machine Learning with Python
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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.
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