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DATASCIENCE & MACHINE LEARNING WITH PYTHON
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.
1463 Satisfied Learners
One time class room registraion to click here Fee 1000/-
Clasroom training batch schedules:
| Start Date | Time | Days | Location | Book Seat |
|---|---|---|---|---|
| 2024-11-02 | 10:00 AM | Weekend | Aundh | Enquiry |
DataScience Training with ML & Python
DataScience Online Training with Global Certification
Duration of Training : 60 hrs
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Data Science & ML With Python Training & Certification in Pune
Highly Experienced Certified Trainer with 15+ yrs Exp. in Industry
Realtime Projects, Scenarios & Assignments
Why Radical Technologies
100% Placement Guarantee for the Right Candidate
10+ Years Real Time Experienced Trainers
Learn from Industry Experts, Hands-on labs
Flexible Options: online, instructor-led, self-paced
14+ Years of Industry Recognitions
1 Lakh+ Students Trained
50,000+ Students Placed
Guaranteed 5+ Interview Calls
Top MNCs - Associated with 800+ Recruiters
Free Internship Project & Certification
Monthly Job Fair - Virtual as well as Physica
5000+ Reviews & Ratings
Learn Data Science, Deep Learning & Machine Learning with Python
Live Machine Learning & Deep Learning Projects
2 Major Projects | 10 Minor Projects | 100+ Assignments
Data Sets, Installations, Interview Preparations, Repeat the session until 6 months are all attractions of this particular course
Trainer : Experienced Data Science Consultant
WANT TO BE A FUTURE DATA SCIENTIST ?
Introduction :
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 analyzing 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.
Data Science, Statistics with Python :
This course Start with introduction to Data Science and Statistics using Python. It covers both the aspects of Statistical concepts and the practical implementation using Python. If you’re new to Python, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.
Analytics :
Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease.
Machine Learning and Data Science :
Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.
Real Life examples :
Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant. finance context.
Target Audience :
- 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.
COURSE CONTENT :
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
- 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 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
- 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
- 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
- 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
- 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 : Predictive Modelling
- 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
- 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
- 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
- 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
- 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.
Related Combo Programs :
Oracle SQL+ Python Scripting + Data science with machine learning
Python Scripting + Data science with machine learning + Deep Learning
Student Stories and Reviews :
FAQs :
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.
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.
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.
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.
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.
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.
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.
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.
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.
Join Radical Technologies for a transformative learning experience in data science, and let us guide you toward a successful career in this dynamic field.
Most Probable Interview Questions for Data Science & Machine Learning with Python
Interview Question No. 1 for Data Science & Machine Learning with Python : Can you explain the difference between supervised and unsupervised learning, and provide examples of each?
Interview Question No. 2 for Data Science & Machine Learning with Python : Describe your experience with data preprocessing techniques in Python, including handling missing values, scaling features, and encoding categorical variables.
Interview Question No. 3 for Data Science & Machine Learning with Python : 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?
Interview Question No. 4 for Data Science & Machine Learning with Python : Can you discuss the advantages and disadvantages of different machine learning algorithms such as decision trees, random forests, and support vector machines?
Interview Question No. 5 for Data Science & Machine Learning with Python : Describe your approach to feature selection and feature engineering in Python, and how you identify and create relevant features for improving model performance.
Interview Question No. 6 for Data Science & Machine Learning with Python : 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.
Interview Question No. 7 for Data Science & Machine Learning with Python : Can you explain the concept of bias-variance tradeoff in machine learning, and how you address it when training and evaluating models in Python?
Interview Question No. 8 for Data Science & Machine Learning with Python : Describe your experience with ensemble learning methods such as bagging, boosting, and stacking, and how you combine multiple models to improve predictive performance.
Interview Question No. 9 for Data Science & Machine Learning with Python : How do you handle imbalanced datasets in Python, and what strategies do you use to address class imbalance in classification tasks?
Interview Question No. 10 for Data Science & Machine Learning with Python : Can you discuss your approach to hyperparameter tuning in Python, including techniques such as grid search, random search, and Bayesian optimization?
Interview Question No. 11 for Data Science & Machine Learning with Python : Describe your experience with time series analysis and forecasting in Python, including techniques for trend analysis, seasonality detection, and model selection.
Interview Question No. 12 for Data Science & Machine Learning with Python : Discuss your approach to text mining and natural language processing (NLP) tasks in Python, including techniques for text preprocessing, sentiment analysis, and named entity recognition.
Interview Question No. 13 for Data Science & Machine Learning with Python : Can you explain the difference between generative and discriminative models in machine learning, and provide examples of each implemented in Python?
Interview Question No. 14 for Data Science & Machine Learning with Python : Describe your experience with dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) in Python.
Interview Question No. 15 for Data Science & Machine Learning with Python : How do you handle missing or incomplete data in Python, and what imputation techniques do you use to fill in missing values?
Interview Question No. 16 for Data Science & Machine Learning with Python : Discuss your experience with deep learning frameworks such as TensorFlow and Keras, and how you implement deep neural networks for image classification and natural language processing tasks.
Interview Question No. 17 for Data Science & Machine Learning with Python : Can you explain the concept of transfer learning in deep learning, and how you leverage pre-trained models for fine-tuning on new datasets in Python?
Interview Question No. 18 for Data Science & Machine Learning with Python : Describe your experience with model deployment and productionization in Python, including techniques for building RESTful APIs and deploying models to cloud platforms such as AWS or Azure.
Interview Question No. 19 for Data Science & Machine Learning with Python : How do you interpret the results of a machine learning model, including feature importance, coefficients, and decision boundaries, to gain insights into the underlying data patterns?
Interview Question No. 20 for Data Science & Machine Learning with Python : Can you provide examples of real-world projects where you’ve applied machine learning techniques in Python to solve business problems and deliver actionable insights?
Learn Data Science & Machine Learning with Python – Course in Pune with Training, Certification & Guaranteed Job Placement Assistance!
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.
Key Features:
- Machine Learning Courses: Dive deep into the realm of machine learning, a crucial component of our Data Science Training in Pune.
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Additional Information:
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Join us to embark on a transformative journey in data science, led by top industry professionals at Radical Technologies, the leader in Data Science Training in Pune.
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