Description
Curriculum
Instructor
Download Course Brochure
Unlock the power of data with our comprehensive, hands-on course designed to take you from foundational data analysis to advanced machine learning and artificial intelligence. Whether you’re looking to start a career in data science or deepen your existing knowledge, this course offers a structured pathway to mastering the tools and techniques that are transforming industries worldwide.
What You Will Learn:
- Data Analysis & Visualization: Develop the skills to analyze and visualize data using powerful tools like Python, R, and SQL. Learn how to clean, manipulate, and explore data to extract meaningful insights.
- Foundations of Data Science: Gain a deep understanding of core data science concepts including statistics, data processing, and feature engineering. Understand how to work with both structured and unstructured data.
- Machine Learning: Dive into supervised and unsupervised learning algorithms, including regression, classification, clustering, and model evaluation. Gain hands-on experience with tools like scikit-learn, TensorFlow, and Keras.
- Deep Learning & AI: Explore advanced techniques in neural networks, deep learning, and artificial intelligence. Learn how to build and deploy models that can recognize patterns, make predictions, and automate tasks.
- Real-World Applications: Work on projects simulating real-world data science and AI problems, applying your knowledge to solve complex challenges in areas like finance, healthcare, e-commerce, and beyond.
Why Choose This Course?
- Expert-Led Instruction: Learn from industry experts with years of experience in data science and AI.
- Hands-On Projects: Get practical experience by working on real-world datasets and building your own machine learning models.
- Flexible Learning: Access the course anytime, anywhere, with lifetime access to materials and resources.
- Career-Ready Skills: Gain the in-demand skills needed to thrive in the rapidly growing fields of data science, machine learning, and AI.
Whether you’re a beginner or looking to expand your expertise, this course will equip you with the knowledge and practical experience to succeed in the exciting world of data science and artificial intelligence.
What’s App us: +91-8799141678
Download Course Brochure
Curriculum
- 10 Sections
- 348 Lessons
- 44 Weeks
Expand all sectionsCollapse all sections
- Data Analysis in Excel38
- 1.0QUICK REVIEW ON MS EXCEL OPTIONS, RIBBON, SHEETS
- 1.1SAVING EXCEL FILE AS PDF, CSV AND OLDER VERSIONS
- 1.2USING EXCEL SHORTCUTS WITH FULL LIST OF SHORTCUTS
- 1.3COPY, CUT, PASTE, HIDE, UNHIDE, AND LINK THE DATA IN ROWS, COLUMNS AND SHEET
- 1.4USING PASTE SPECIAL OPTIONS FORMATTING CELLS, ROWS, COLUMNS AND SHEETS
- 1.5DATA VALIDATION IN EXCEL
- 1.6ADVANCED FILTERING IN EXCEL
- 1.7GROUPING & SUBTOTAL IN EXCEL
- 1.8PROTECTING & UNPROTECTING CELLS, ROWS, COLUMNS AND SHEETS WITH OR WITHOUT PASSWORD
- 1.9PAGE LAYOUT AND PRINTER PROPERTIES
- 1.10WORKING WITH FORMULAS / FUNCTION
- 1.11LOOKUP AND REFERENCE FUNCTIONS: VLOOKUP, HLOOKUP, INDEX, MATCH, ETC
- 1.12LOGICAL FUNCTION: IF / ELSE, AND, OR, NOT, TRUE, NESTED IF/ELSE ETC
- 1.13DATE & TIME FUNCTIONS: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS, NOW, TODAY MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY
- 1.14MATH & TRIGONOMETRY FUNCTIONS: RAND, ROUND, CEILING, FLOOR, INT, LCM, EVEN, SUMIF
- 1.15STATISTICAL FUNCTIONS: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA, COUNTBLANK, COUNTIF, MAX, MAXA,MIN, MINA, STDEVA
- 1.16TEXT FUNCTIONS: LEFT, RIGHT, TEXT, TRIM, MID, LOWER, UPPER, PROPER, REPLACE, REPT, FIND, SEARCH, SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE.
- 1.17USING CONDITIONAL FORMATTING WITH MULTIPLE CELL RULES
- 1.18USING COLOR SCALES AND ICON SETS IN CONDITIONAL FORMATTING
- 1.19CREATING NEW RULES AND MANAGING EXISTING RULES
- 1.20DATA LOCK & PROTECTION
- 1.21ADVANCE CHARTS IN EXCEL
- 1.22AREA CHARTS AND SURFACE CHARTS
- 1.23TREND LINE CHARTS AND CANDLE STICK
- 1.24XY (SCATTER CHARTS)
- 1.25TIME SERIES CHARTS AND BUBBLE CHARTS
- 1.26RADAR CHARTS AND DOUGHNUT CHARTS
- 1.27WORKING WITH FILL SERIES & GO TO SPECIAL
- 1.28CONSOLIDATION IN EXCEL
- 1.29GOAL SEEK & SCENARIO
- 1.30MANAGER & DATA TABLE
- 1.31PROFESSIONAL DASHBOARDS IN EXCEL
- 1.32INTERACTIVE SALES DASHBOARD IN EXCEL
- 1.33BANK & MARKETING DASHBOARD
- 1.34INTERACTIVE HR DASHBOARD IN EXCEL
- 1.35PROJECT MANAGEMENT DASHBOARD IN EXCEL
- 1.36CALL CENTRE DASHBOARD IN EXCEL
- 1.37WORKING WITH HISTOGRAM
- Database Design with MySQL21
- 2.0INTRODUCTION
- 2.1WHAT IS A DATA WAREHOUSE?
- 2.2ENTITY CONSTRAINTS
- 2.3DDL AND DML STATEMENTS
- 2.4QUERYING IN MYSQL
- 2.5SQL STATEMENTS AND OPERATORS
- 2.6AGGREGATE FUNCTIONS
- 2.7ORDERING
- 2.8STRING AND DATE-TIME FUNCTIONS
- 2.9REGULAR EXPRESSIONS
- 2.10NESTED QUERIES
- 2.11VIEWS in SQL
- 2.12JOINS AND SET OPERATIONS
- 2.13SET THEORY
- 2.14TYPES OF JOINS
- 2.15TYPES OF JOINS: A DEMONSTRATION
- 2.16OUTER JOINS: A DEMONSTRATION
- 2.17VIEWS WITH JOINS
- 2.18SET OPERATIONS WITH SQL
- 2.19ASSIGNMENT
- 2.20CASE STUDY
- DATA ANALYSIS IN PYTHON41
- 3.0NUMPY IN PYTHON
- 3.1INTRODUCTION TO NUMPY
- 3.2BASICS OF NUMPY
- 3.3MATHEMATICAL OPERATIONS ON NUMPY
- 3.4MATHEMATICAL OPERATIONS ON NUMPY II
- 3.5CREATING NUMPY ARRAYS
- 3.6OPERATIONS OVER 1-D ARRAYS
- 3.7MULTIDIMENSIONAL ARRAYS
- 3.8COMPUTATION TIMES IN NUMPY VS PYTHON
- 3.9PANDAS IN PYTHON
- 3.10INTRODUCTION TO PANDAS
- 3.11BASICS OF PANDAS
- 3.12PANDAS – ROWS AND COLUMNS
- 3.13DESCRIBING DATA
- 3.14INDEXING AND SLICING
- 3.15OPERATIONS ON DATA FRAMES
- 3.16GROUP AND AGGREGATE FUNCTIONS
- 3.17MERGING DATA FRAMES
- 3.18DATA VISUALISATION IN PYTHON
- 3.19INTRODUCTION TO DATA VISUALISATION WITH MATPLOTLIB
- 3.20THE NECESSITY OF DATA VISUALISATION VISUALISATIONS – SOME EXAMPLES
- 3.21FACTS AND DIMENSIONS
- 3.22BAR GRAPH
- 3.23SCATTER PLOT
- 3.24LINE GRAPH AND HISTOGRAM
- 3.25OUTLIERS ANALYSIS WITH BOXPLOTS
- 3.26SUBPLOTS
- 3.27CHOOSING PLOT TYPES
- 3.28PROJECTS
- 3.29DATA VISUALISATION IN PYTHON WITH SEABORN
- 3.30INTRODUCTION
- 3.31DISTRIBUTION PLOTS & STYLING OPTIONS
- 3.32PIE – CHART AND BAR CHART
- 3.33SCATTER PLOTS & PAIR PLOTS
- 3.34REVISITING BAR GRAPHS AND BOX PLOTS
- 3.35HEATMAPS
- 3.36LINE CHARTS
- 3.37STACKED BAR CHARTS
- 3.38CASE STUDY SUMMARY
- 3.39DATA VISUALISATION PRACTICE
- 3.40CASE STUDY
- Statistics Essentials10
- R Programming30
- 5.0R–Overview
- 5.1R – Environment Setup
- 5.2R – Basic Syntax
- 5.3R – Data Types
- 5.4R – Variables
- 5.5R – Operators
- 5.6R – Decision Making
- 5.7R – Loops & R – Function
- 5.8R – Strings & R – Lists
- 5.9R – Matrices
- 5.10R – Arrays & R – Factors
- 5.11R – Data Frames
- 5.12R – Packages
- 5.13R – Data Reshaping Melt The Data
- 5.14R – Csv Files
- 5.15R – Excel File Input As Xlsx
- 5.16R – Binary Files
- 5.17R – Xml Files
- 5.18R – Json File
- 5.19R – Web Data
- 5.20R – Databases
- 5.21R – Pie Charts & R – Bar Charts
- 5.22R – Boxplots & R – Histograms
- 5.23R – Scatterplots
- 5.24R – Mean, Median & Mode
- 5.25R – Linear Regression
- 5.26R – Multiple Regression
- 5.27R – Logistic Regression
- 5.28R – Normal Distribution
- 5.29R – 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
- 7.0DEFINE THE BUSINESS PROBLEM – BUSINESS
- 7.1PUBLIC AND PRIVATE DATA
- 7.2DATA SOURCING
- 7.3UNDERSTANDING RAW DATA
- 7.4DATA CLEANING
- 7.5FILTERING DATA
- 7.6FIXING ROWS AND COLUMNS
- 7.7STANDARDISING VALUES
- 7.8MISSING VALUES
- 7.9INVALID VALUES
- 7.10INTRODUCTION TO EDA
- 7.11PREPARING DATA FOR ANALYSIS
- 7.12DATA ANALYSIS: MODELLING
- 7.13MODEL EVALUATION
- 7.14MODEL DEPLOYMENT
- 7.15ASSIGNMENT
- 7.16UNIVARIATE ANALYSIS
- 7.17UNORDERED CATEGORICAL VARIABLES
- 7.18QUANTITATIVE VARIABLES – UNIVARIATE ANALYSIS
- 7.19QUANTITATIVE VARIABLES – SUMMARY METRICS
- 7.20SEGMENTED UNIVARIATE
- 7.21BASIS OF SEGMENTATION
- 7.22QUICK WAY OF SEGMENTATION
- 7.23COMPARISON OF AVERAGES
- 7.24COMPARISON OF OTHER METRICS
- 7.25BIVARIATE ANALYSIS
- 7.26BIVARIATE 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
- 8.0INDUSTRIAL CASE STUDIES
- 8.1CASE STUDY ON FORMATTING THE DATA
- 8.2COST CALCULATION CASE STUDY
- 8.3CASE STUDY ON SHOE COMPANY
- 8.4DATA FILTERING MINI CHALLENGE
- 8.5ADVANCED CHARTS CASE STUDY
- 8.6DATA CLEANING & PREAPRING IN EXCEL
- 8.7ADVANCE PIVOT BASED REPORT GENERATION
- 8.8MAKING REPORT ON HOTEL BOOKING
- 8.9SALES CASE STUDY FOR A TOY COMPANY
- 8.10EDA PROJECTS
- 8.11PROJECT ON DRUG REVIEWS DATASET
- 8.12PROJECT ON IPL / FOOTBALL DATA ANALYSES
- 8.13PROJECT ON EXPLORING FACTORS OF LIFE
- 8.14EXPECTANCY
- 8.15PROJECT ON TIME SERIES FORECAST ON ENERGY CONSUMPTION
- 8.16PROJECT ON LOAN PREDICTION
- 8.17PROJECT ON HOME PRICE PREDICTION
- 8.18PROJECT ON OTT ( NETFLIX / HOTSTAR DISNEY )
- DATA SCIENCE AND AI60
- 9.0ANALYTICS PROBLEM SOLVING
- 9.1PYTHON FOR DATA SCIENCE
- 9.2HYPOTHESIS TESTING
- 9.3CONCEPTS OF HYPOTHESIS TESTING – I: NULL AND ALTERNATE HYPOTHESIS, MAKING A DECISION, AND CRITICAL VALUE METHOD
- 9.4CONCEPTS OF HYPOTHESIS TESTING – II: P VALUE METHOD AND TYPES OF ERRORS
- 9.5INDUSTRY DEMONSTRATION OF HYPOTHESIS TESTING: TWO-SAMPLE MEAN AND PROPORTION TEST, A/B TESTING
- 9.6LINEAR REGRESSION
- 9.7SIMPLE LINEAR REGRESSION
- 9.8SIMPLE LINEAR REGRESSION IN PYTHON
- 9.9MULTIPLE LINEAR REGRESSION
- 9.10MULTIPLE LINEAR REGRESSION IN PYTHON
- 9.11INDUSTRY RELEVANCE OF LINEAR REGRESSION
- 9.12LOGISTIC REGRESSION
- 9.13UNIVARIATE LOGISTIC REGRESSION
- 9.14MULTIVARIATE LOGISTIC REGRESSION:
- 9.15MODEL BUILDING AND EVALUATION
- 9.16LOGISTIC REGRESSION: INDUSTRY APPLICATIONS
- 9.17CLASSIFICATION USING DECISION TREES
- 9.18INTRODUCTION TO DECISION TREES
- 9.19HYPERPARAMETER TUNING IN DECISION TREES
- 9.20ALGORITHMS FOR DECISION TREES CONSTRUCTION
- 9.21UNSUPERVISED LEARNING
- 9.22CLUSTERING
- 9.23INTRODUCTION TO CLUSTERING
- 9.24K-MEANS CLUSTERING
- 9.25HIERARCHICAL CLUSTERING
- 9.26OTHER FORMS OF CLUSTERING: K-MODE, K-PROTOTYPE, DB SCAN
- 9.27BASICS OF NLP AND TEXT MINING
- 9.28REGEX AND INTRODUCTION TO NLP
- 9.29BASIC LEXICAL PROCESSING
- 9.30ADVANCED LEXICAL PROCESSING
- 9.31BUSINESS PROBLEM SOLVING
- 9.32INTRODUCTION TO BUSINESS PROBLEM SOLVING
- 9.33BUSINESS PROBLEM SOLVING: CASE STUDY DEMONSTRATIONS
- 9.34Learn how to approach open ended, real- world problems using data as a lever to draw actionable insights.
- 9.35CASE STUDY: LEAD SCORING
- 9.36PROBLEM STATEMENT
- 9.37INTRODUCTION TO RANDOM FORESTS
- 9.38HIERARCHICAL CLUSTERING
- 9.39FEATURE IMPORTANCE IN RANDOM FORESTS
- 9.40RANDOM FORESTS IN PYTHON
- 9.41Learn how powerful ensemble algorithms can improve your classification models by building random forests from decision trees
- 9.42BOOSTING
- 9.43INTRODUCTION TO BOOSTING AND ADABOOST
- 9.44GRADIENT BOOSTING
- 9.45PRINCIPAL COMPONENT ANALYSIS
- 9.46SINGULAR VALUE DECOMPOSITION
- 9.47ADVANCED REGRESSION
- 9.48GENERALISED LINEAR REGRESSION
- 9.49REGULARISED REGRESSION
- 9.50ADVANCED ML CASE STUDY
- 9.51MODEL SELECTION & GENERAL ML TECHNIQUES
- 9.52PRINCIPLES OF MODEL SELECTION
- 9.53MODEL EVALUATION
- 9.54MODEL SELECTION: BEST PRACTICES
- 9.55TIME SERIES FORECASTING
- 9.56INTRODUCTION TO TIME SERIES AND ITS COMPONENTS
- 9.57SMOOTHING TECHNIQUES
- 9.58INTRODUCTION TO AR MODELS
- 9.59BUILDING AR MODELS
- Natural Language Processing (NLP)67
- 10.0FUNDAMENTALS OF NLP
- 10.1Text Preprocessing: Techniques for cleaning and preparing text (tokenization, stemming, lemmatization, stop-word removal)
- 10.2Text Representation: Converting words to numerical representations (Bag of Words, TF-IDF, Word Embeddings).
- 10.3WORD EMBEDDINGS
- 10.4Word2Vec, GloVe, and FastText: Learn how words are embedded into vector space, capturing semantic meaning.
- 10.5Contextual Embeddings: Explore advanced methods like ELMo, BERT, and GPT, which consider the context of words.
- 10.6SEQUENCE MODELS
- 10.7RNNs (Recurrent Neural Networks): Learn how RNNs handle sequential data such as sentences.
- 10.8LSTMs and GRUs: Explore advanced RNN models for better handling long-range dependencies in text.
- 10.9ATTENTION MECHANISMS & TRANSFORMERS
- 10.10Attention Mechanism: Understand how attention models focus on relevant parts of a sentence.
- 10.11Transformers: Learn the foundation of models like BERT and GPT that revolutionized NLP with non sequential processing.
- 10.12LANGUAGE MODELS
- 10.13Pretrained Models (BERT, GPT, etc.): Study large models that are pretrained on vast corpora, allowing for fine-tuning on specific NLP tasks.
- 10.14Transfer Learning: Using pretrained models for specific applications like text classification or translation.
- 10.15TEXT GENERATION
- 10.16Sequence-to-Sequence Models: Understand how models generate text, such as in translation, summarization, or chatbot conversations.
- 10.17Generative Models: Explore GPT and similar models for generating human-like text.
- 10.18SENTIMENT ANALYSIS AND TEXT CLASSIFICATION
- 10.19Sentiment Analysis: Techniques for analyzing text for emotions or opinions.
- 10.20Text Classification: Methods for categorizing text into predefined classes (e.g., spam detection, topic classification).
- 10.21NAMED ENTITY RECOGNITION
- 10.22(NER) AND PART-OF-SPEECH
- 10.23(POS) TAGGING
- 10.24NER: Extract important entities (people, places, dates) from text.
- 10.25POS Tagging: Assign grammatical categories (nouns, verbs) to words in a sentence.
- 10.26SPEECH AND TEXT SYNTHESIS
- 10.27Text-to-Speech (TTS): Convert written text into spoken words.
- 10.28Speech Recognition: Use models to convert spoken language into written text.
- 10.29DEEP LEARNING
- 10.30NEURAL NETWORKS
- 10.31FUNDAMENTALS
- 10.32Perceptrons and Multilayer Networks: Basics of neural networks, how neurons work, and the architecture of feedforward neural networks (FNNs).
- 10.33Activation Functions: Explore functions like ReLU, Sigmoid, and Tanh, used to introduce non linearity in neural networks.
- 10.34BACKPROPAGATION AND OPTIMIZATION
- 10.35Gradient Descent: Learn how neural networks are trained using backpropagation and optimization techniques like stochastic gradient descent (SGD) and Adam.
- 10.36Loss Functions: Study different loss functions such as mean squared error (MSE) and cross-entropy for classification and regression tasks.
- 10.37DEEP LEARNING
- 10.38DEEP LEARNING ARCHITECTURES
- 10.39Fully Connected Networks (FNNs): Learn about the structure of deep networks where each neuron connects to every neuron in the next layer.
- 10.40Convolutional Neural Networks (CNNs): Understand CNNs for image recognition, object detection, and computer vision tasks.
- 10.41Recurrent Neural Networks (RNNs): Study RNNs and their variants (LSTM, GRU) for handling sequential data like time series or text.
- 10.42REGULARIZATION TECHNIQUES
- 10.43Overfitting Solutions: Learn techniques like dropout, L2 regularization, and batch normalization to improve generalization and prevent overfitting.
- 10.44DEEP LEARNING
- 10.45TRANSFER LEARNING
- 10.46Pretrained Models: Utilize pre trained models (e.g., VGG, ResNet, BERT) and fine-tune them for specific tasks, saving time and computational resources.
- 10.47AUTOENCODERS AND REPRESENTATION LEARNING
- 10.48Autoencoders: Learn how autoencoders compress data into lower-dimensional representations for tasks like anomaly detection and unsupervised learning.
- 10.49SEQUENCE MODELS AND ATTENTION
- 10.50RNNs, LSTMs, GRUs: Explore models for sequential data like time-series prediction, language translation, and speech recognition.
- 10.51Attention Mechanism: Learn how attention is used in sequence models to focus on important parts of input data.
- 10.52Transformers: Study transformer architecture for handling long-range dependencies, the backbone of models like BERT and GPT.
- 10.53DEEP LEARNING
- 10.54GENERATIVE MODELS
- 10.55Generative Adversarial Networks (GANs): Understand how GANs generate new data similar to a given dataset, such as generating realistic images.
- 10.56Variational Autoencoders (VAEs): Learn VAEs for probabilistic modeling and generating new, high quality data samples.
- 10.57COMPUTER VISION
- 10.58Image Processing with CNNs: Use CNNs for image classification, object detection, and segmentation tasks.
- 10.59Advanced Vision Techniques: Learn transfer learning, data augmentation, and fine-tuning for improving model performance on vision tasks.
- 10.60Text Processing: Study deep learning models for text-based applications like language modeling, text classification, sentiment analysis, and machine translation.
- 10.61REINFORCEMENT LEARNING
- 10.62RL Algorithms: Understand how deep learning is used in reinforcement learning for decision making and optimizing tasks over time, such as in games or robotics.
- 10.63DEPLOYING AND SCALING DEEP
- 10.64LEARNING MODELS
- 10.65Model Deployment: Learn how to deploy trained deep learning models for real-world applications using frameworks like TensorFlow, PyTorch, and ONNX.
- 10.66Scaling and Optimization: Use techniques for distributed training and model optimization to handle large datasets and improve inference speed.
Yash Jain
0 Students47 Courses
You might be interested in
₹ 18,000.00₹ 15,000.00
-
All levels
₹ 18,000.00₹ 15,000.00
Graphic Design Courses in Surat | learn Adobe Photoshop | Learn Graphic design | Video Editing Course near me Illustrator...
₹ 15,000.00₹ 13,000.00
-
All levels
₹ 15,000.00₹ 13,000.00
Graphic Design Courses in Surat | learn Adobe Photoshop | Learn Graphic design | Video Editing Course near me Illustrator...
₹ 23,000.00₹ 18,000.00
-
All levels
₹ 23,000.00₹ 18,000.00
Graphic Design Courses in Surat | learn Adobe Photoshop | Learn Graphic design | Video Editing Course near me Illustrator...
₹ 15,000.00₹ 11,000.00
-
All levels
₹ 15,000.00₹ 11,000.00
Graphic Design Courses in Surat | learn Adobe Photoshop | Learn Graphic design | Video Editing Course near me Illustrator...
Sign up to receive our latest updates
Get in touch
Quick Links
- Contact Us
- About Us
- Privacy Policy
- Terms & Conditions
