Advanced Certificate in Data Analysis, Data Science & AI course
Advanced Certificate in Data Analysis, Data Science & AI
The Advanced Certificate in Data Analysis, Data Science & Artificial Intelligence Course is designed to help students build strong analytical, programming, and artificial intelligence skills. In today’s data-driven world, organizations rely on data scientists and analysts to extract insights, automate decision-making, and develop intelligent systems.
In this course, students learn the fundamentals of data analysis, statistics, machine learning, and artificial intelligence using modern programming tools and frameworks. The program focuses on transforming raw data into meaningful insights through data cleaning, visualization, modeling, and predictive analytics.
Students gain hands-on experience with tools such as Excel, Python, R Programming, Power BI, NumPy, Pandas, Matplotlib, and Machine Learning frameworks, helping them analyze large datasets and develop AI-driven solutions. According to the course overview on page 1, the program teaches how machine learning and deep learning algorithms analyze data and create intelligent systems that mimic human decision-making.
Data Analysis & Data Science
The course begins with data analysis fundamentals, where students learn how to collect, clean, analyze, and visualize data to discover patterns and insights.
Students learn important analytical tools including:
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Data Analysis in Excel
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Database Design & SQL
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Data Analysis in Python
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Data Visualization using Matplotlib and Seaborn
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Statistics and Probability
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R Programming for Data Analysis
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Business Intelligence using Power BI
The course walkthrough section (page 3) highlights how students learn to convert raw data into knowledge through analysis, visualization, and communication.
Machine Learning & Artificial Intelligence
Students are introduced to advanced Machine Learning and AI algorithms used in predictive analytics and intelligent systems.
Topics include:
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Linear Regression
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Logistic Regression
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Decision Trees
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Random Forest
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Boosting Algorithms
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Principal Component Analysis (PCA)
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Time Series Forecasting
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Natural Language Processing (NLP)
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Deep Learning Models
The program also covers Deep Learning architectures such as neural networks, CNNs, RNNs, and transformers, which are used in applications like image recognition, text processing, and intelligent automation.
Who Can Join
Students & Beginners
Students who want to start learning data analysis, data science, and AI technologies.
Computer Science & IT Students
Students pursuing BCA, BSc IT, Engineering, or Diploma programs who want to strengthen their data and AI skills.
Aspiring Data Scientists
Individuals interested in careers in data analytics, machine learning, and artificial intelligence.
Working Professionals
Professionals who want to upgrade their skills and transition into data science or AI roles.
Career Opportunities
After completing this program, students can explore multiple high-demand careers in data and AI fields.
Data Analyst
Business Intelligence Analyst
Data Scientist
Machine Learning Engineer
AI Engineer
Data Visualization Engineer
Database Engineer
Financial Analyst
Risk Analyst
Data Engineer
The career path diagram on page 2 shows roles such as Data Analyst, AI & ML Engineer, Data Scientist, and Business Intelligence Analyst as common career outcomes.
Why Choose This Course
Complete Data Science Curriculum
Covers data analysis, machine learning, deep learning, and artificial intelligence technologies.
Industry-Relevant Tools
Hands-on training with Excel, Python, R Programming, Power BI, NumPy, Pandas, and AI frameworks.
Real-World Projects
Students work on real datasets such as loan prediction, home price prediction, OTT data analysis, and time series forecasting projects.
Practical Machine Learning Training
Learn regression models, clustering, NLP, and advanced machine learning algorithms.
Career Preparation
Students receive training in resume building, LinkedIn profile optimization, and interview preparation for data roles.
Frequently Asked Questions (FAQ)
What is the duration of the Data Science & AI course?
The course duration typically ranges from 6 to 8 months depending on the training schedule and project work.
Do I need programming knowledge before joining?
Basic computer knowledge is helpful, but no prior programming experience is mandatory.
What tools and technologies will I learn?
Students learn Excel, SQL, Python, R Programming, Power BI, Machine Learning algorithms, and Deep Learning techniques.
Will I work on real-world projects?
Yes. Students complete multiple industry case studies and projects using real datasets.
What career paths are available after this course?
Students can pursue careers as Data Analysts, Data Scientists, Machine Learning Engineers, and AI Engineers.
Why Choose Us ?
Over 19 Years of Experience
100% Practical Training
Industrial Projects
1 on 1 Mentorship
Rated 4.9 on Google
Resume Feedback
Focus on Practical Skills
100% Placement Assistance
Flexible Timings
Over 19 Years of Experience
100% Practical Training
Industrial Projects
1 on 1 Mentorship
Rated 4.9 on Google
Resume Feedback
Focus on Practical Skills
100% Placement Assistance
Flexible Timings
What Our Students Have To Say About Us

i feel more confident using basic programs and shortcuts now. the teachers are friendly too. overall, it was an engaging and useful learning experience.


I have completed my python programming course with great learning!!






It is an amazing experience here studying new things and enjoyed learning
Overall great experience, and recommend for someone starting afresh.
If you're planning to pursue a computer course, I highly recommend visiting Future Vision. All the faculty members are very cooperative, and I want to extend a special thanks to Yash Sir for building my confidence and supporting me throughout the journey.
Thank you, Future Vision, for making this learning experience so enriching.
The institute provided detail learning about graphics. It helped me to sharpen my skills . Highly recommend!
Thanks





Sir Yash made even the tough parts easy to understand, and the hands-on practice really helped me improve my skills. I now feel much more confident using Excel and creating dashboards in Power BI. Highly recommend this course!

Sir Yash made even the tough parts easy to understand, and the hands-on practice really helped me improve my skills. I now feel much more confident using Excel and creating dashboards in Power BI. Highly recommend this course!

The training was clear, practical,and easy to understand.The teacher explained everything step by step nd was always ready to help.I feel more confident using the computer now.

The training was clear, practical,and easy to understand.The teacher explained everything step by step nd was always ready to help.I feel more confident using the computer now.










Yash sir is very professional, he teaches everything very calmly and class environment was also very good
Yash sir is very professional, he teaches everything very calmly and class environment was also very good
Future Vision isn’t just a tuition center—it’s a place where students truly understand and enjoy learning about computers and technology. Whether it’s Informatics Practices or other computer-related courses, the teaching here is structured, engaging, and designed to make complex concepts easy to grasp.
What sets Future Vision apart is its focus on practical learning. Instead of just memorizing theory, students get hands-on experience, which builds real skills they can use in the future. The instructors are patient, knowledgeable, and always ready to help, making learning a stress-free and enjoyable process.
Beyond academics, Future Vision creates a positive and motivating environment where students feel encouraged to ask questions, explore new ideas, and build confidence in their abilities. It’s a place that truly prepares students for a tech-driven future, making learning both meaningful and exciting.
Future Vision isn’t just a tuition center—it’s a place where students truly understand and enjoy learning about computers and technology. Whether it’s Informatics Practices or other computer-related courses, the teaching here is structured, engaging, and designed to make complex concepts easy to grasp.
What sets Future Vision apart is its focus on practical learning. Instead of just memorizing theory, students get hands-on experience, which builds real skills they can use in the future. The instructors are patient, knowledgeable, and always ready to help, making learning a stress-free and enjoyable process.
Beyond academics, Future Vision creates a positive and motivating environment where students feel encouraged to ask questions, explore new ideas, and build confidence in their abilities. It’s a place that truly prepares students for a tech-driven future, making learning both meaningful and exciting.
Personal attention is been provided by tutors.
It was a good experience learning at future vision.
Personal attention is been provided by tutors.
It was a good experience learning at future vision.
Great experience
Loved the course and learnt alot
also the explanation was very detailed.
Great experience
Loved the course and learnt alot
also the explanation was very detailed.













The course material provided was comprehensive and well-organized. The coaching classes provided lecture notes, practice exercises, and additional resources like code samples and reference materials.
The course emphasized practical application through coding exercises and mini-projects.
Considering the quality of teaching, course content, and overall learning experience, I believe the course offered excellent value for money.
Overall, I highly recommend the Python Programming course at the future vision computer institute . It is suitable for beginners and individuals with some prior programming experience looking to expand their knowledge of Python.

The course material provided was comprehensive and well-organized. The coaching classes provided lecture notes, practice exercises, and additional resources like code samples and reference materials.
The course emphasized practical application through coding exercises and mini-projects.
Considering the quality of teaching, course content, and overall learning experience, I believe the course offered excellent value for money.
Overall, I highly recommend the Python Programming course at the future vision computer institute . It is suitable for beginners and individuals with some prior programming experience looking to expand their knowledge of Python.




















Siddharth sir's expertise and knowledge in the arena made learning even more profound and enjoyable.
He has been nothing but patient with me, allowing me rectify my mistakes and helping me learn the technicality of the functions, formulas and usage with a greater depth.
I would surely recommend you to join future vision and enroll in the plethora of courses they offer.
Siddharth sir's expertise and knowledge in the arena made learning even more profound and enjoyable.
He has been nothing but patient with me, allowing me rectify my mistakes and helping me learn the technicality of the functions, formulas and usage with a greater depth.
I would surely recommend you to join future vision and enroll in the plethora of courses they offer.
Was amazing experience.
Was amazing experience.






My experience was good and I will surely suggest you to take classes from here.
My experience was good and I will surely suggest you to take classes from here.


Because Siddharth sir is best sir ever.
He's studing method is so deferent.
Because Siddharth sir is best sir ever.
He's studing method is so deferent.
Harsh Patel
Harsh Patel
Mr Yash who explained everything in such a detail that you will learn everything there only. He is really confident in his work and his concepts are crystal clear.
Environment of the place there is very warm.
This place is highly recommended to the ones looking for any computer courses. They have all basic and advanced learning programs.
Mr Yash who explained everything in such a detail that you will learn everything there only. He is really confident in his work and his concepts are crystal clear.
Environment of the place there is very warm.
This place is highly recommended to the ones looking for any computer courses. They have all basic and advanced learning programs.
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- 10 Sections
- 348 Lessons
- 44 Weeks
- Data Analysis in Excel38
- 1.1QUICK REVIEW ON MS EXCEL OPTIONS, RIBBON, SHEETS
- 1.2SAVING EXCEL FILE AS PDF, CSV AND OLDER VERSIONS
- 1.3USING EXCEL SHORTCUTS WITH FULL LIST OF SHORTCUTS
- 1.4COPY, CUT, PASTE, HIDE, UNHIDE, AND LINK THE DATA IN ROWS, COLUMNS AND SHEET
- 1.5USING PASTE SPECIAL OPTIONS FORMATTING CELLS, ROWS, COLUMNS AND SHEETS
- 1.6DATA VALIDATION IN EXCEL
- 1.7ADVANCED FILTERING IN EXCEL
- 1.8GROUPING & SUBTOTAL IN EXCEL
- 1.9PROTECTING & UNPROTECTING CELLS, ROWS, COLUMNS AND SHEETS WITH OR WITHOUT PASSWORD
- 1.10PAGE LAYOUT AND PRINTER PROPERTIES
- 1.11WORKING WITH FORMULAS / FUNCTION
- 1.12LOOKUP AND REFERENCE FUNCTIONS: VLOOKUP, HLOOKUP, INDEX, MATCH, ETC
- 1.13LOGICAL FUNCTION: IF / ELSE, AND, OR, NOT, TRUE, NESTED IF/ELSE ETC
- 1.14DATE & TIME FUNCTIONS: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS, NOW, TODAY MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY
- 1.15MATH & TRIGONOMETRY FUNCTIONS: RAND, ROUND, CEILING, FLOOR, INT, LCM, EVEN, SUMIF
- 1.16STATISTICAL FUNCTIONS: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA, COUNTBLANK, COUNTIF, MAX, MAXA,MIN, MINA, STDEVA
- 1.17TEXT FUNCTIONS: LEFT, RIGHT, TEXT, TRIM, MID, LOWER, UPPER, PROPER, REPLACE, REPT, FIND, SEARCH, SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE.
- 1.18USING CONDITIONAL FORMATTING WITH MULTIPLE CELL RULES
- 1.19USING COLOR SCALES AND ICON SETS IN CONDITIONAL FORMATTING
- 1.20CREATING NEW RULES AND MANAGING EXISTING RULES
- 1.21DATA LOCK & PROTECTION
- 1.22ADVANCE CHARTS IN EXCEL
- 1.23AREA CHARTS AND SURFACE CHARTS
- 1.24TREND LINE CHARTS AND CANDLE STICK
- 1.25XY (SCATTER CHARTS)
- 1.26TIME SERIES CHARTS AND BUBBLE CHARTS
- 1.27RADAR CHARTS AND DOUGHNUT CHARTS
- 1.28WORKING WITH FILL SERIES & GO TO SPECIAL
- 1.29CONSOLIDATION IN EXCEL
- 1.30GOAL SEEK & SCENARIO
- 1.31MANAGER & DATA TABLE
- 1.32PROFESSIONAL DASHBOARDS IN EXCEL
- 1.33INTERACTIVE SALES DASHBOARD IN EXCEL
- 1.34BANK & MARKETING DASHBOARD
- 1.35INTERACTIVE HR DASHBOARD IN EXCEL
- 1.36PROJECT MANAGEMENT DASHBOARD IN EXCEL
- 1.37CALL CENTRE DASHBOARD IN EXCEL
- 1.38WORKING WITH HISTOGRAM
- Database Design with MySQL21
- 2.1INTRODUCTION
- 2.2WHAT IS A DATA WAREHOUSE?
- 2.3ENTITY CONSTRAINTS
- 2.4DDL AND DML STATEMENTS
- 2.5QUERYING IN MYSQL
- 2.6SQL STATEMENTS AND OPERATORS
- 2.7AGGREGATE FUNCTIONS
- 2.8ORDERING
- 2.9STRING AND DATE-TIME FUNCTIONS
- 2.10REGULAR EXPRESSIONS
- 2.11NESTED QUERIES
- 2.12VIEWS in SQL
- 2.13JOINS AND SET OPERATIONS
- 2.14SET THEORY
- 2.15TYPES OF JOINS
- 2.16TYPES OF JOINS: A DEMONSTRATION
- 2.17OUTER JOINS: A DEMONSTRATION
- 2.18VIEWS WITH JOINS
- 2.19SET OPERATIONS WITH SQL
- 2.20ASSIGNMENT
- 2.21CASE STUDY
- DATA ANALYSIS IN PYTHON41
- 3.1NUMPY IN PYTHON
- 3.2INTRODUCTION TO NUMPY
- 3.3BASICS OF NUMPY
- 3.4MATHEMATICAL OPERATIONS ON NUMPY
- 3.5MATHEMATICAL OPERATIONS ON NUMPY II
- 3.6CREATING NUMPY ARRAYS
- 3.7OPERATIONS OVER 1-D ARRAYS
- 3.8MULTIDIMENSIONAL ARRAYS
- 3.9COMPUTATION TIMES IN NUMPY VS PYTHON
- 3.10PANDAS IN PYTHON
- 3.11INTRODUCTION TO PANDAS
- 3.12BASICS OF PANDAS
- 3.13PANDAS – ROWS AND COLUMNS
- 3.14DESCRIBING DATA
- 3.15INDEXING AND SLICING
- 3.16OPERATIONS ON DATA FRAMES
- 3.17GROUP AND AGGREGATE FUNCTIONS
- 3.18MERGING DATA FRAMES
- 3.19DATA VISUALISATION IN PYTHON
- 3.20INTRODUCTION TO DATA VISUALISATION WITH MATPLOTLIB
- 3.21THE NECESSITY OF DATA VISUALISATION VISUALISATIONS – SOME EXAMPLES
- 3.22FACTS AND DIMENSIONS
- 3.23BAR GRAPH
- 3.24SCATTER PLOT
- 3.25LINE GRAPH AND HISTOGRAM
- 3.26OUTLIERS ANALYSIS WITH BOXPLOTS
- 3.27SUBPLOTS
- 3.28CHOOSING PLOT TYPES
- 3.29PROJECTS
- 3.30DATA VISUALISATION IN PYTHON WITH SEABORN
- 3.31INTRODUCTION
- 3.32DISTRIBUTION PLOTS & STYLING OPTIONS
- 3.33PIE – CHART AND BAR CHART
- 3.34SCATTER PLOTS & PAIR PLOTS
- 3.35REVISITING BAR GRAPHS AND BOX PLOTS
- 3.36HEATMAPS
- 3.37LINE CHARTS
- 3.38STACKED BAR CHARTS
- 3.39CASE STUDY SUMMARY
- 3.40DATA VISUALISATION PRACTICE
- 3.41CASE STUDY
- Statistics Essentials10
- R Programming30
- 5.1R–Overview
- 5.2R – Environment Setup
- 5.3R – Basic Syntax
- 5.4R – Data Types
- 5.5R – Variables
- 5.6R – Operators
- 5.7R – Decision Making
- 5.8R – Loops & R – Function
- 5.9R – Strings & R – Lists
- 5.10R – Matrices
- 5.11R – Arrays & R – Factors
- 5.12R – Data Frames
- 5.13R – Packages
- 5.14R – Data Reshaping Melt The Data
- 5.15R – Csv Files
- 5.16R – Excel File Input As Xlsx
- 5.17R – Binary Files
- 5.18R – Xml Files
- 5.19R – Json File
- 5.20R – Web Data
- 5.21R – Databases
- 5.22R – Pie Charts & R – Bar Charts
- 5.23R – Boxplots & R – Histograms
- 5.24R – Scatterplots
- 5.25R – Mean, Median & Mode
- 5.26R – Linear Regression
- 5.27R – Multiple Regression
- 5.28R – Logistic Regression
- 5.29R – Normal Distribution
- 5.30R – Binomial Distribution
- Microsoft Power BI29
- 6.1COMPONENTS OF POWER BI: DESKTOP, SERVICE, AND MOBILE APPS
- 6.2BENEFITS AND APPLICATIONS OF POWER BI
- 6.3POWER BI ARCHITECTURE OVERVIEW
- 6.4INSTALLING AND SETTING UP POWER BI DESKTOP
- 6.5CONNECTING TO DATA SOURCES (EXCEL, CSV, ONLINE SERVICES, ETC.)
- 6.6CLEANING AND TRANSFORMING DATA
- 6.7HANDLING MISSING DATA SPLITTING AND MERGING COLUMNS
- 6.8DATA FORMATTING AND STANDARDIZATION WORKING WITH RELATIONSHIPS BETWEEN TABLES
- 6.9CREATING CUSTOM COLUMNS AND MEASURES WITH DAX (DATA ANALYSIS EXPRESSIONS)
- 6.10CREATING RELATIONSHIPS BETWEEN TABLES UNDERSTANDING
- 6.11CALCULATED COLUMNS AND MEASURES
- 6.12MANAGING MODEL PERFORMANCE WITH OPTIMIZATION TECHNIQUES
- 6.13CREATING BASIC CHARTS: BAR, LINE, PIE, AND COLUMN CHARTS
- 6.14ADVANCED VISUALS: SCATTER PLOTS, MAPS, FUNNEL CHARTS, & GAUGES USING SLICERS, FILTERS, & DRILL-THROUGHS.
- 6.15CREATING AND CUSTOMIZING DASHBOARDS FORMATTING AND STYLING REPORTS FOR BETTER USER EXPERIENCE USING POWER BI MARKETPLACE FOR CUSTOM VISUALS
- 6.16CALCULATED COLUMNS AND MEASURES
- 6.17AGGREGATION FUNCTIONS (SUM, AVERAGE, COUNT)
- 6.18TIME INTELLIGENCE FUNCTIONS (YTD, MTD, QTD)
- 6.19PUBLISHING REPORTS TO POWER BI SERVICE
- 6.20CREATING DASHBOARDS IN POWER BI SERVICE
- 6.21SHARING REPORTS AND DASHBOARDS WITH OTHERS
- 6.22EXPORTING REPORTS TO PDF
- 6.23SALES ANALYSIS
- 6.24FINANCIAL REPORTING
- 6.25CONNECTING POWER BI DESKTOP WITH MOBILE FOR REAL TIME ANALYSIS
- 6.26RETAIL ANALYSIS DASHBOARDS
- 6.27TELECOM CHURN RATE DASHBOARD
- 6.28OLA COMPANY DASHBOARD
- 6.29BLINKIT ANALYSIS DASHBOARD
- Understanding EDA (THE CRISP-DM FRAMEWORK)33
- 7.1DEFINE THE BUSINESS PROBLEM – BUSINESS
- 7.2PUBLIC AND PRIVATE DATA
- 7.3DATA SOURCING
- 7.4UNDERSTANDING RAW DATA
- 7.5DATA CLEANING
- 7.6FILTERING DATA
- 7.7FIXING ROWS AND COLUMNS
- 7.8STANDARDISING VALUES
- 7.9MISSING VALUES
- 7.10INVALID VALUES
- 7.11INTRODUCTION TO EDA
- 7.12PREPARING DATA FOR ANALYSIS
- 7.13DATA ANALYSIS: MODELLING
- 7.14MODEL EVALUATION
- 7.15MODEL DEPLOYMENT
- 7.16ASSIGNMENT
- 7.17UNIVARIATE ANALYSIS
- 7.18UNORDERED CATEGORICAL VARIABLES
- 7.19QUANTITATIVE VARIABLES – UNIVARIATE ANALYSIS
- 7.20QUANTITATIVE VARIABLES – SUMMARY METRICS
- 7.21SEGMENTED UNIVARIATE
- 7.22BASIS OF SEGMENTATION
- 7.23QUICK WAY OF SEGMENTATION
- 7.24COMPARISON OF AVERAGES
- 7.25COMPARISON OF OTHER METRICS
- 7.26BIVARIATE ANALYSIS
- 7.27BIVARIATE ANALYSIS ON CONTINUOUS VARIABLES
- 7.28BUSINESS PROBLEMS INVOLVING CORRELATION
- 7.29BIVARIATE ANALYSIS ON CATEGORICAL VARIABLES
- 7.30DERIVED METRICS
- 7.31TYPES DRIVEN METRICS
- 7.32BUSINESS DRIVEN METRICS
- 7.33DATA DRIVEN METRICS
- EDA Projects & Case Study19
- 8.1INDUSTRIAL CASE STUDIES
- 8.2CASE STUDY ON FORMATTING THE DATA
- 8.3COST CALCULATION CASE STUDY
- 8.4CASE STUDY ON SHOE COMPANY
- 8.5DATA FILTERING MINI CHALLENGE
- 8.6ADVANCED CHARTS CASE STUDY
- 8.7DATA CLEANING & PREAPRING IN EXCEL
- 8.8ADVANCE PIVOT BASED REPORT GENERATION
- 8.9MAKING REPORT ON HOTEL BOOKING
- 8.10SALES CASE STUDY FOR A TOY COMPANY
- 8.11EDA PROJECTS
- 8.12PROJECT ON DRUG REVIEWS DATASET
- 8.13PROJECT ON IPL / FOOTBALL DATA ANALYSES
- 8.14PROJECT ON EXPLORING FACTORS OF LIFE
- 8.15EXPECTANCY
- 8.16PROJECT ON TIME SERIES FORECAST ON ENERGY CONSUMPTION
- 8.17PROJECT ON LOAN PREDICTION
- 8.18PROJECT ON HOME PRICE PREDICTION
- 8.19PROJECT ON OTT ( NETFLIX / HOTSTAR DISNEY )
- DATA SCIENCE AND AI60
- 9.1ANALYTICS PROBLEM SOLVING
- 9.2PYTHON FOR DATA SCIENCE
- 9.3HYPOTHESIS TESTING
- 9.4CONCEPTS OF HYPOTHESIS TESTING – I: NULL AND ALTERNATE HYPOTHESIS, MAKING A DECISION, AND CRITICAL VALUE METHOD
- 9.5CONCEPTS OF HYPOTHESIS TESTING – II: P VALUE METHOD AND TYPES OF ERRORS
- 9.6INDUSTRY DEMONSTRATION OF HYPOTHESIS TESTING: TWO-SAMPLE MEAN AND PROPORTION TEST, A/B TESTING
- 9.7LINEAR REGRESSION
- 9.8SIMPLE LINEAR REGRESSION
- 9.9SIMPLE LINEAR REGRESSION IN PYTHON
- 9.10MULTIPLE LINEAR REGRESSION
- 9.11MULTIPLE LINEAR REGRESSION IN PYTHON
- 9.12INDUSTRY RELEVANCE OF LINEAR REGRESSION
- 9.13LOGISTIC REGRESSION
- 9.14UNIVARIATE LOGISTIC REGRESSION
- 9.15MULTIVARIATE LOGISTIC REGRESSION:
- 9.16MODEL BUILDING AND EVALUATION
- 9.17LOGISTIC REGRESSION: INDUSTRY APPLICATIONS
- 9.18CLASSIFICATION USING DECISION TREES
- 9.19INTRODUCTION TO DECISION TREES
- 9.20HYPERPARAMETER TUNING IN DECISION TREES
- 9.21ALGORITHMS FOR DECISION TREES CONSTRUCTION
- 9.22UNSUPERVISED LEARNING
- 9.23CLUSTERING
- 9.24INTRODUCTION TO CLUSTERING
- 9.25K-MEANS CLUSTERING
- 9.26HIERARCHICAL CLUSTERING
- 9.27OTHER FORMS OF CLUSTERING: K-MODE, K-PROTOTYPE, DB SCAN
- 9.28BASICS OF NLP AND TEXT MINING
- 9.29REGEX AND INTRODUCTION TO NLP
- 9.30BASIC LEXICAL PROCESSING
- 9.31ADVANCED LEXICAL PROCESSING
- 9.32BUSINESS PROBLEM SOLVING
- 9.33INTRODUCTION TO BUSINESS PROBLEM SOLVING
- 9.34BUSINESS PROBLEM SOLVING: CASE STUDY DEMONSTRATIONS
- 9.35Learn how to approach open ended, real- world problems using data as a lever to draw actionable insights.
- 9.36CASE STUDY: LEAD SCORING
- 9.37PROBLEM STATEMENT
- 9.38INTRODUCTION TO RANDOM FORESTS
- 9.39HIERARCHICAL CLUSTERING
- 9.40FEATURE IMPORTANCE IN RANDOM FORESTS
- 9.41RANDOM FORESTS IN PYTHON
- 9.42Learn how powerful ensemble algorithms can improve your classification models by building random forests from decision trees
- 9.43BOOSTING
- 9.44INTRODUCTION TO BOOSTING AND ADABOOST
- 9.45GRADIENT BOOSTING
- 9.46PRINCIPAL COMPONENT ANALYSIS
- 9.47SINGULAR VALUE DECOMPOSITION
- 9.48ADVANCED REGRESSION
- 9.49GENERALISED LINEAR REGRESSION
- 9.50REGULARISED REGRESSION
- 9.51ADVANCED ML CASE STUDY
- 9.52MODEL SELECTION & GENERAL ML TECHNIQUES
- 9.53PRINCIPLES OF MODEL SELECTION
- 9.54MODEL EVALUATION
- 9.55MODEL SELECTION: BEST PRACTICES
- 9.56TIME SERIES FORECASTING
- 9.57INTRODUCTION TO TIME SERIES AND ITS COMPONENTS
- 9.58SMOOTHING TECHNIQUES
- 9.59INTRODUCTION TO AR MODELS
- 9.60BUILDING AR MODELS
- Natural Language Processing (NLP)67
- 10.1FUNDAMENTALS OF NLP
- 10.2Text Preprocessing: Techniques for cleaning and preparing text (tokenization, stemming, lemmatization, stop-word removal)
- 10.3Text Representation: Converting words to numerical representations (Bag of Words, TF-IDF, Word Embeddings).
- 10.4WORD EMBEDDINGS
- 10.5Word2Vec, GloVe, and FastText: Learn how words are embedded into vector space, capturing semantic meaning.
- 10.6Contextual Embeddings: Explore advanced methods like ELMo, BERT, and GPT, which consider the context of words.
- 10.7SEQUENCE MODELS
- 10.8RNNs (Recurrent Neural Networks): Learn how RNNs handle sequential data such as sentences.
- 10.9LSTMs and GRUs: Explore advanced RNN models for better handling long-range dependencies in text.
- 10.10ATTENTION MECHANISMS & TRANSFORMERS
- 10.11Attention Mechanism: Understand how attention models focus on relevant parts of a sentence.
- 10.12Transformers: Learn the foundation of models like BERT and GPT that revolutionized NLP with non sequential processing.
- 10.13LANGUAGE MODELS
- 10.14Pretrained Models (BERT, GPT, etc.): Study large models that are pretrained on vast corpora, allowing for fine-tuning on specific NLP tasks.
- 10.15Transfer Learning: Using pretrained models for specific applications like text classification or translation.
- 10.16TEXT GENERATION
- 10.17Sequence-to-Sequence Models: Understand how models generate text, such as in translation, summarization, or chatbot conversations.
- 10.18Generative Models: Explore GPT and similar models for generating human-like text.
- 10.19SENTIMENT ANALYSIS AND TEXT CLASSIFICATION
- 10.20Sentiment Analysis: Techniques for analyzing text for emotions or opinions.
- 10.21Text Classification: Methods for categorizing text into predefined classes (e.g., spam detection, topic classification).
- 10.22NAMED ENTITY RECOGNITION
- 10.23(NER) AND PART-OF-SPEECH
- 10.24(POS) TAGGING
- 10.25NER: Extract important entities (people, places, dates) from text.
- 10.26POS Tagging: Assign grammatical categories (nouns, verbs) to words in a sentence.
- 10.27SPEECH AND TEXT SYNTHESIS
- 10.28Text-to-Speech (TTS): Convert written text into spoken words.
- 10.29Speech Recognition: Use models to convert spoken language into written text.
- 10.30DEEP LEARNING
- 10.31NEURAL NETWORKS
- 10.32FUNDAMENTALS
- 10.33Perceptrons and Multilayer Networks: Basics of neural networks, how neurons work, and the architecture of feedforward neural networks (FNNs).
- 10.34Activation Functions: Explore functions like ReLU, Sigmoid, and Tanh, used to introduce non linearity in neural networks.
- 10.35BACKPROPAGATION AND OPTIMIZATION
- 10.36Gradient Descent: Learn how neural networks are trained using backpropagation and optimization techniques like stochastic gradient descent (SGD) and Adam.
- 10.37Loss Functions: Study different loss functions such as mean squared error (MSE) and cross-entropy for classification and regression tasks.
- 10.38DEEP LEARNING
- 10.39DEEP LEARNING ARCHITECTURES
- 10.40Fully Connected Networks (FNNs): Learn about the structure of deep networks where each neuron connects to every neuron in the next layer.
- 10.41Convolutional Neural Networks (CNNs): Understand CNNs for image recognition, object detection, and computer vision tasks.
- 10.42Recurrent Neural Networks (RNNs): Study RNNs and their variants (LSTM, GRU) for handling sequential data like time series or text.
- 10.43REGULARIZATION TECHNIQUES
- 10.44Overfitting Solutions: Learn techniques like dropout, L2 regularization, and batch normalization to improve generalization and prevent overfitting.
- 10.45DEEP LEARNING
- 10.46TRANSFER LEARNING
- 10.47Pretrained Models: Utilize pre trained models (e.g., VGG, ResNet, BERT) and fine-tune them for specific tasks, saving time and computational resources.
- 10.48AUTOENCODERS AND REPRESENTATION LEARNING
- 10.49Autoencoders: Learn how autoencoders compress data into lower-dimensional representations for tasks like anomaly detection and unsupervised learning.
- 10.50SEQUENCE MODELS AND ATTENTION
- 10.51RNNs, LSTMs, GRUs: Explore models for sequential data like time-series prediction, language translation, and speech recognition.
- 10.52Attention Mechanism: Learn how attention is used in sequence models to focus on important parts of input data.
- 10.53Transformers: Study transformer architecture for handling long-range dependencies, the backbone of models like BERT and GPT.
- 10.54DEEP LEARNING
- 10.55GENERATIVE MODELS
- 10.56Generative Adversarial Networks (GANs): Understand how GANs generate new data similar to a given dataset, such as generating realistic images.
- 10.57Variational Autoencoders (VAEs): Learn VAEs for probabilistic modeling and generating new, high quality data samples.
- 10.58COMPUTER VISION
- 10.59Image Processing with CNNs: Use CNNs for image classification, object detection, and segmentation tasks.
- 10.60Advanced Vision Techniques: Learn transfer learning, data augmentation, and fine-tuning for improving model performance on vision tasks.
- 10.61Text Processing: Study deep learning models for text-based applications like language modeling, text classification, sentiment analysis, and machine translation.
- 10.62REINFORCEMENT LEARNING
- 10.63RL Algorithms: Understand how deep learning is used in reinforcement learning for decision making and optimizing tasks over time, such as in games or robotics.
- 10.64DEPLOYING AND SCALING DEEP
- 10.65LEARNING MODELS
- 10.66Model Deployment: Learn how to deploy trained deep learning models for real-world applications using frameworks like TensorFlow, PyTorch, and ONNX.
- 10.67Scaling and Optimization: Use techniques for distributed training and model optimization to handle large datasets and improve inference speed.
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