Example Image with Text
Use this Image with Text block to balance out your text content with a complementary visual to strengthen messaging and help your students connect with your product, course, or coaching. You can introduce yourself with a profile picture and author bio, showcase a student testimonial with their smiling face, or highlight an experience with a screenshot.
Example Text
Use this Text block to tell your course or coaching’s story.
Write anything from one-liners to detailed paragraphs that tell your visitors more about what you’re selling.
This block - along with other blocks that contain text content - supports various text formatting such as header sizes, font styles, alignment, ordered and unordered lists, hyperlinks and colors.
Example Title
Use this block to showcase testimonials, features, categories, or more. Each column has its own individual text field. You can also leave the text blank to have it display nothing and just showcase an image.
Example Title
Use this block to showcase testimonials, features, categories, or more. Each column has its own individual text field. You can also leave the text blank to have it display nothing and just showcase an image.
Example Title
Use this block to showcase testimonials, features, categories, or more. Each column has its own individual text field. You can also leave the text blank to have it display nothing and just showcase an image.
Example Curriculum
Linux Training
Available in
days
days
after you enroll
- Day 1 - Why we need an OS | Basic commands in linux | Interrupt Signals | Ctrl key shortcuts | Date command and its options | Man command | How to run any command/program in background | Creating the file and directory | Uses of Pipe symbol (222:23)
- Summary
- Day 2 - Shells in Linux | Local host | Remote host | What is Virtualisation | Bridge adaptor | Ping command | Ifconfig command | Setting up remote host | What is Ssh protocol | What is a process | Ps-aux | Process id | X11 program to launch graphical program | Enabling ssh root login (210:21)
- Summary
- Day 3 - What is software/package | rpm command | How to check software behind any command | Installing and uninstalling softwares Check softwares in the ISO | What is yum | Yum configuration | How to switch to root user | Vi editor | Vi editor options (154:37)
- Summary
- 4.What is docker | Docker Installation | Different ways of installing OS | Docker host | What is Containerization | What is Container | Docker ps command | Docker pull command | Docker run command | Giving name to a container | Docker start command | Docker attach command (197:19)
- Summary
- 5. script command | running coomand without login on remote system | absolute path | cd .. command | / directory | scp commmand | docker info command | docker rm command | mounting external drive to docker container (202:40)
- Summary
- 6.different type of server | Apache web server | Install the client software | CLI and GUI | HTTP Server | Web server configuraton | Protocols | Web client setup (213:55)
- Summary
- 7. Install podman | custom images | container os | install python3 | docker file | environment | container file | workspace | interactive and non interactive command | docker file keyword | build | docker hub | docker pull | docker push | docker registory | repository | Standzation (175:57)
- Summary
- 8. Install python | docker file | cmd keyword | versioning | build time | run time | history keyword | copy keyword | and keyword | mount code | Entrypoint keyword | docker file keyword (217:32)
- Summary
- 9. sudo concept | root power | sudoer file | sodu command | non- interactive command | password | install httpd | detached | Apache web server | selinux | cgi-bin | backend services (84:33)
- Summary
- 10. Networking | LAN/ NIC/N/W / Ethernet Card | Wireless | IP address | fishing Attack | key network | mac address | octet | bytes | 4 octet | DNS server | look up | IPV4 | binary calculater | layer 7 firewall | server | network program | port number (75:13)
- Summary
- 11. Linux Adminstration V9 Training By Mr. Vimal Daga on 5th May 2024_GMT20240605-053001 (128:41)
- Summary 11
- 12. port number | pating | nating | kubernetes | fault tolerance | availability | container management tool | orchestration tool | kubernetes service | Minikube | kubectl command | cluster info | pod | launch pod | deployment | cri- (79:01)
- 12.1_Industry Expert Session by ChandraShekhar_GMT20240607-064639_Recording_1920x1080 (119:01)
- 13. Linux Adminstration V9 Training By Mr. Vimal Daga_GMT20240613-082801 (66:10)
- 14. Linux Adminstration V9 Training By Mr. Vimal Daga_GMT20240614-085029 (59:31)
Bonus Sessions - Linux
Available in
days
days
after you enroll
- Plain Text | Cipher Text | Private and Public Key | Symmetric and Asymmetric Key | ssh | Key Based Authentication | User Management (217:10)
- Useradd Command | adduser Command | GECOS | chfn Command | man Command | finger Command | who Command | w Command | Home Directory | home directory | Bash Shell | Nologin Shell | Shell Program | Interactive Users | Non interactive Users (138:11)
- Superuser | General User | System or Service User | Hashing | Epoch Time | Password Aging
- User Permissions| Permissions on a file | Permissions on a directory | Modes | chmod Command | chown Command | su Command | Groups | chgrp Command
- DAC (Discretionary Access Control) | Linux Permissions | Special Permissions | Sticky Bit | SGID | SUID | Challenge of 'w' Permission | Set sticky bit on a folder | Primary Group | Secondary Group | newgrp command | gpasswd command | Use case of SUID (147:23)
- POSIX | ACL | getfacl Command | setfacl Command | Mask | Effective Permissions | Umask | Sudo Power| Admin Level Commands | System Level Commands | /etc/sudoers main configuration file | /etc/sudoers.d secondary configuration file | Wheel Group | Visudo (172:25)
- Security Program | Firewall | Network Service | Network Traffic | Firewalld Service | Pre-created rules (Zones) | Custom rules | firewall-cmd Command | Network Interface Card (NIC) | Implement Firewalld on NIC Target | Zones | PAT | Masquerade | Port Forwarding | Rich Rules (194:50)
- Basic concepts of Partition | Hard Disk | Different types of Hard Disk | Steps to create Virtual Hard Disk (100:24)
- Create a Partition (100:08)
- Partition | Format | Mount (94:25)
- LVM | Extend the LV (103:24)
- LVM | Reduce LV (79:12)
Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables
Available in
days
days
after you enroll
- Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables (114:19)
- Word Press | MySQL | Set-up Three Tier Architecture | Patting | Hosting a Webpage | Container Linking (122:16)
- Docker Networking | NAT | Networking Basics | SDN | Network Infrastructure | Bridge (129:28)
- Runtime | Plugins | NAT | Bridge Network Interface | SDN | DHCP | DNS | Subnet | Gateway | IPAM | Custom Network Infrastructure (135:17)
Docker Bonus Session
Available in
days
days
after you enroll
- Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables (114:19)
- Word Press | MySQL | Set-up Three Tier Architecture | Patting | Hosting a Webpage | Container Linking (122:16)
- Docker Networking | NAT | Networking Basics | SDN | Network Infrastructure | Bridge (129:28)
- Runtime | Plugins | NAT | Bridge Network Interface | SDN | DHCP | DNS | Subnet | Gateway | IPAM | Custom Network Infrastructure (135:17)
- PID | Why Docker is Superfast? | Need of OS | Process | Nested Process | Pgrep Command | /proc directory | Bash Shell | Kernel | Cgroup (98:19)
Python Training
Available in
days
days
after you enroll
- Day 1 - Why we need a programming language | Print() function |variables | System() function | Storing multiple data| Data structures | Tuple data structure | List data structure | Len() function | Difference between list and tuple | Slicing operator| Numpy library| Array data structure| Pip command (156:01)
- Summary
- Day 2 - Dictionary | Pandas | Dataframe | iloc function | identation | if-else | input function | creating menu like program to select from multiple options | Os module (143:54)
- Summary
- Day 3 - creating Function | Right use case of functions | parameterized function | positional arguments | readability of the code | os module | not operator | for loop | and operator | subprocess.getouput() function (141:12)
- Summary
- 4. Lis | Tuple | Functions | variables | Array | one-dimensional array | two-dimensional array (matrix) | OpenCV library | 3d array (144:23)
- Summary
- 5. Brainstorming (79:30)
- 6. Boolean | data type | Function | Variable | Keyword | Special keyword | Operator | For loop | String | While loop | Library (120:31)
- Summary
- 7. Gateway interface | CGI program | API | Http protocol | Rest API | Aws cloud | Ec2 service | firewall | launch Ec2 instance | public IP | create web application | web server | install httpd | URL | which command | program file | Executable command | http header | http body | content type (150:54)
- Summary
- 8. Cgi-bin | Web server | input function | search engine | q variable | query string | import cgi | field storage function | security group | ssh login | cell injection | pre tag | html form | action tag | test Api | postman tool | install postman | crud operation | get method | put method (159:32)
- Summary
- 8.1_Python Programming Training By Mr. Vimal DagaGMT20240528-103753 (112:18)
- 9. Custom data structure | Template| list | Initiatioztion | reference | Multi-threding | parallel | inheritance | single inheritance | base class | drive class | Multiple inheritance | constructor | crud operation | public variable | private keyword | access modifier concept (277:45)
- Summary
- 10. Python Programming Training By Mr. Vimal Daga_GMT20240530-104732 (212:48)
- Summary 10.
- 11. cvzone | Tzdata | Cv2 | hand tracking | hand Detector | Find hand | hand landmarks | import os | if else program | pose tracking (215:47)
- Summary 11
- 12. Class | Object | Memory | RAM | Variable | Function (149:02)
- Summary
- 13. Variable | Function | Object | Class | Keyword | Get method | Set method | Custom data structure | List | Constructor (72:36)
- Summary
- 14. MongoDB | Storage devices | database management system | SQL language | unstructured data | Document oriented data base | row oriented data base | graph databases | Crud operation | CLI & GUI & python | mongo shell | compass | protocol | connections string | data format | get collection (142:03)
- Summary
- 15. pymongo | dir function | mongo client | database | drop database | get database | create database | collection | find method | insert one | cursor | find all | json format | for loop | file handling | operators | $gt | $gte | while loop | constructor | multi tier architecture (128:17)
- Summary
- 16. Python Programming Training By Mr. Vimal Daga_GMT20240613-114805 (109:43)
- 17. Python Programming Training By Mr. Vimal Daga_GMT20240618-114205 (49:39)
Bonus Session - Python
Available in
days
days
after you enroll
- Lists | text-to-speech | pyttsx3 library | speaker driver | converting strings to audio | DataFrame | resolving row-wise operation limitations faced in dictionaries | Pandas library | loc[] function | .csv files | read_csv() function | describe() function (101:20)
- Summary - Lists | text-to-speech | pyttsx3 library
- list in Python | memory address | Copy module | shallow and deep copy | deepcopy() function | 2-D & 3-D lists | Functions | Iteration | for loop (105:39)
- Summary - list in Python | memory address
- Menu program | integration of Python and Linux | If-else conditions | while loops| system| or OS commands in Python | tput setaf <0-7> command | OS module | triple quotes (“””) | input() function | (92:04)
- keyword |all types of containers: lists, dicts, sets, strings | File handling | manipulate file | two categories, text file, and binary file | handle file exceptions | append mode| Reading a file | read() | readline() | function tell ()| Closing a file | (125:34)
- Summary - keyword |all types of containers: lists, dicts, sets, strings
- File handling | read mode ("r"), | write mode ("w") | append mode ("a") | open() function | tell() method | file position: | Seek() function | (92:15)
- Summary - File handling | read mode ("r"),
- Object Oriented Programming | Classes |data structure. | object | instantiation | data member |member functions | Access Modifiers | Public | Private | Setter and Getter | (90:45)
- Summary - Object Oriented Programming | Classes |data structure
- Open() function | hard disk is located | “With” keyword | “speech_recognition” | “Pyaudio”| text.lower() | Google Recognize API | (117:59)
- Module | function (def) | call function | OS module | if __name__ | "__main__" | infinite loop | (88:35)
- Anonymous function |lambda keyword.| adding two numbers function | modulo operator | modulo operator| filter() function | built-in function | database | (90:28)
- Server | Socket programming. | Networking program | IP + port Number means | Bind function | netstat -tnip command. | client | (85:45)
- Summary - Server | Socket programming. | Networking program
- Server side code | client side program | network programming | conn variable | conn.recv | os.system | (70:00)
- Summary - Server side code | client side program
- Server | Socket programming | handle input and output | external sources | IP and port number|while" loop (97:18)
- Summary - Server | Socket programming | handle input and output
- local IP address | Network programming | Variable|string data | Client and server| while loop (67:16)
- Summary - local IP address | Network programming
- Threading | Multi-threading | RAM | CPU | Stack memory | function (72:08)
- Exception handling | File handling | close() | read() | write() | (88:37)
- iteration | TTS | pyttsx3 | program file | speaker | (134:36)
Machine Learning Training
Available in
days
days
after you enroll
- Day 1 -What is machine learning | machine learning works | How to think and find the formula | Artificial General Intelligence | What are Features | What is Feature Selection | Feature elimination | Dependent Variable | Independent Variable | Co-efficient | Hit and trial method | Loss function (107:23)
- Summary
- Day 2 -What is a dataset | Linear regression | Scikit learn library | What are Features | Dependent and independent variables | Pandas Library | Why to use pandas | Model fitting | Converting 1D data into 2D data | What is Supervised learning | Joblib library (120:51)
- Summary
- 3. Activity (173:33)
- 4. Accuracy | linear regression | pandas | convert 1D to 2D | coefficient | bias concept | visualization technique | data point | historical data | slope | best fit line | simple linear regression | EDA technique | Matplotlib | scatter function | plot() function | intercept | parameter | residuals (114:59)
- Summary
- 5. Error | Loss function | cost function | Mean Absolute Error (MAE) | Learning Curve | Slope | weightage | bias | mean_absolute_error function | model selection (110:58)
- Summary
- 6. Multi-Linear Regression | Statistical Analysis | Features | weight | Categorical Variable | Handling Categorical Variables | One Hot Encoding | Dummy Variable | Dummy Variable trap (104:09)
- Summary
- 7. dummy variable trap | independent variables | Categorical | test set | training Set | Data Preprocessing | train test split | random state (57:22)
- Summary
- 8. supervised learning | CLassification | Regression | Binary Classification | Sigmoid Function | Logestic Regression | Titanic Data set | Seaborn Library | EDA (138:45)
- Summary
- 9. confusion matrix | split dataset | train test split | random state | true positive | true negative | false positive | false negative | actual value | predicted value | type 1 and type 2 error (111:50)
- Summary
- 10. confusion matrix | split dataset | train test split | random state | true positive | true negative | false positive | false negative | actual value | predicted value | type 1 and type 2 error (120:48)
- Summary
- 11. What is deep learning | Pattern | What is neuron | Input layer | Hidden layer | Output layer | Activation function | Feedforward neural network (121:19)
- Summary
- 12. Tensorflow library | Keras library | Neuron | Neural networks | Artificial neural network | Activation function | Perceptron | Creating own neural network | Dense function | Data imputation | Loss function (91:48)
- Summary
- 13. Churm modelling dataset | Brain | Nodes | Training model for churn modelling | Supervised learning | Sequential model | Relu activation function | Sigmoid activation | function | Input layer | Hidden layer (114:10)
- Summary
- 14. Creating model for churn modelling | Feature engineering on churn dataset | Multi-layer perceptron | Kernel initialiser | Glorot uniform initialiser (101:44)
- Summary
- 15. Wines dataset | Creating model for wines dataset | One-hot encoding | Multicollinearity | Softmax activation function | Output attribute | Input attribute (80:38)
- Summary
- 16. Training on wines dataset | HeNormal initialisation | Input_dim | Softmax activation function | Categorical crossentropy loss | Adam optimizer | Matrics | Epochs (90:56)
- Summary
- 17. Machine Learning Training By Mr. Vimal Daga (93:11)
- Summary 17
- 18. Auto feature extraction | convolution | pixel | records | stride | convore layer | feature map | convolution2D | Sequential | kernel size | brain lobes | occipital lobes | activation function | input data | input shape | 3D image | pooling | mean pooling | max pooling | average pooling (109:33)
- Summary
Bonus Session - MLOps Training
Available in
days
days
after you enroll
Prompt Engineering Training
Available in
days
days
after you enroll
- 1. prompt engineering | What is Generative AI | Contexts | Long term memory | What is LSTM | Context keyword | models | What is LLM | OpenAI | Hallucination problem | What is fine tuning | OpenAI playground | Some basic prompts example | Giving roles to the chatgpt | What is system role (281:01)
- Summary 1
- 2. User role | System role | Assistant role | LLM module | API Call | ChatGPT | openai play ground | Temperature | parameters | openai library | chat competition model | context window | GPT-3. 5 turbo | Tokens | Token Id | Embedding model | open ai Tokenizer | pricing | Tokens | Maximum Tokens (184:19)
- Summary 2
- 3. Generative | zero shot problem | QNA type prompting | few shot prompting | Chain of thought | fine tuning | think step by step | input keyword | contact (238:59)
- Summary 3
Deep Learning Sessions
Available in
days
days
after you enroll
- Session 1-Artificial Neural Networks (ANN)|machine learning model|human brain and neurons|accuracy |feature selection|ANN function|layers | (213:38)
- Session 2-dataset|churn modelling|accuracy|chat-gpt|perception|multi-layer|feedforward neural network| (153:12)
- Session 3-multi class classification in deep learning|sigmoid|wine dataset (107:07)
- Session 4-computer vision in deep learning|cvzone|module|face detection module|hand tracking module (138:54)
- Session 5-create code for face detection|multi-factor authentication|face recognition| (71:20)
- Session 6-record|Introduction to Convolutional Neural Networks (CNN)| (75:22)
- Session 7-create code for Convolutional Neural Networks (CNN) (96:38)
- Session 8-pooling layer|convolution layer|flatten|dense|sequential| (113:22)
- Session 9-Introduction of recurrent neural network (RNN) (120:52)
- Session 10-data preprocessing|one dimensionalneural network|time series|google stock price test data set (93:12)
- Session 11-create code for google stock price test data set (67:15)
NLP Training
Available in
days
days
after you enroll
- Session 1 - 4th Sept-Difference between NLP and nlu | sentiment analysis | implementation of textblob | polarity | subjectivity | objectivity (218:30)
- Session 2 - 5th Sept-Implementation on dataset | data handling with pandas | textblob implementation on data (165:28)
- Session 3 - 6th Sept-Tokenization | lemmatization | stemming | implementation of nltk (310:18)
- Session 4 - 7th Sept-nltk installation | WordNet | Spacy | Automatic Conversion from uppercase to lowercase (113:47)
- Session 5 - 11th Sept-Revision Session (66:16)
- Session 6 - 12th Sept-NLU |introduction of spacy |token ID|read token|mapping| (101:52)
Certified Kubernetes Administrator & Certified Kubernetes Application Developer
Available in
days
days
after you enroll
- Introduction to Kubernetes (57:56)
- Lab setup (21:20)
- Minikube setup document
- Creating pod (27:29)
- YAML language (16:55)
- Launching first pod from YAML file (45:20)
- Summary
- Labels (50:47)
- Replicas (47:24)
- Summary
- Service - Load balancers (72:51)
- Summary
- Types of Load balancers (50:03)
- Summary
- Types of Services (31:22)
- Node port & Cluster IP (48:02)
- Summary
- Environmental Variables (36:42)
- Replica set (33:15)
- Summary
- Storage in container (44:46)
- Storage in kubernetes (65:41)
- Summary
- Storage Class (48:16)
- Provisioner (47:46)
- Summary
- Deployment (89:54)
- Summary
- Secret (12:54)
- Kustomize (47:52)
- Summary
- Namespace (21:17)
DEVOPS Tools - Mastering Jenkins Session By Mr. Vimal Daga Feb 24
Available in
days
days
after you enroll
- 1.Why Jenkins ? | Jenkins Alternative Tool | Testing automation tool | SCM -GitHub | DevOps in Industry | CI/CD | Pull & Push Code | End To End Process | Reprovisioning | Java In Jenkins | Automation tools | Private & Public Key Concept | DevOps methodology | Jenkins Installation (127:21)
- Summary - Session 1
- 1.1 Revision session (59:29)
- 2. " Why do we Need Jenkins? | Manual Setup & Automated Setup Concept | Jobs/Item/Projects In Jenkins | Sudo Power to Jenkins | Privileges Concepts | Interactive & Non-Interactive Commands | Web-Server Configuration Via Jenkins | Permission Concepts In Linux | SCM-GitHub | GitHub Repository (181:42)
- Summary Session-2
- 3. Importance Of Plugins | Multi-System Concept | Distributed Scheduling Concept | Jobs(Executors) | Credentials In Jnekins | Master-Slave Setup | Pre-requisites For Slave-Configuration | Slave Agent Program | Workspace/Root-Directory | Launch Container Via Jenkins | SCM-Github (216:15)
- Summary - Session 3
- 4. Working Difference of Master & Slave Node | Uses Of Triggers | Deployment Strategies | End To End Automation Concept | Integration of Jenkins with K8S & GitHub | Code push to GitHub via GitBash | Poll SCM | GitHub-Webhook | Image push To docker hub | Test Cases-Manual & Automated (197:03)
- Summary - Session 4
- 5. Slave nodes | Jobs | Parameter | Function | String parameter | Default value | Job ID | Environment veriable | Build number variable | Job name variable | SCM tools | Build the Docker image | Docker file | Docker hub | Overwrite image | Rollback | Trigger | Multiple stages | Post build actions (153:54)
- Summary - Session 5
- 5.1 Revision Session (67:53)
- 6. Pipeline As A code | Permanent Agent(Slave) | Dynamic Provisioning | Docker Plugin | Cloud | Daemon | Security Groups | Docker Image | Docker Hub | Slave Node Via SSH | Slave Node Configuration | JDK Setup In Node | Image Pull From DockerHub | Jenkins Docker Agent | Pull Strategy Of Agent | (165:30)
- Summary - Session 6
- 7. " Automations | Dependency | Job creation | WebUI, CLI and API | Job/Pipeline | Create job Automatically | Create a pipeline Automatically | Pipeline as code (PAC) | Launch Jenkins in kubernetes | Expose Jenkins | Jenkins pod | Logs command | Pipeline plugin | SCM tools (183:28)
- Summary - Session 7
- Jenkins Session (112:18)
Jenkins Extra Sessions
Available in
days
days
after you enroll
- What is Devops ? (27:38)
- Continuous deployment | Continuous testing | Continuous Integration (74:49)
- Maven integration with Jenkins (79:20)
- Windows as a Slave node (36:07)
- Jenkins Integration with Kubernetes (128:36)
- Different ways to interact with Jenkins | Environmental variable| IAM & RBAC in Jenkins| Environmental variable (88:56)
AWS Training
Available in
days
days
after you enroll
- Session 1 - 27th July-cloud computing|EC2, S3, and Lamda|data centers| availability zonesoperating system| launch an instance on AWS (310:06)
- Session 2 - 28th July-Python shell|Lambda functions|serverless services|SNS|FaaS (205:20)
- Session 3 - 29th July-WebUI, CLI, and API| IAM (Identity and Access Management).|Create user|AdministrationAccess (215:10)
- Session 4 - 30th July-create lambda function|staas|storage device/block|S3 (205:34)
- Session 5 - 31st July-integrate S3,SNS,and lambda|SNS service (209:24)
Git and GitHub
Available in
days
days
after you enroll
GitLab Sessions
Available in
days
days
after you enroll
- Session 1 - GitLab Introduction | CI-Pipeline | Pipeline As A Code | Jobs | GitLab Runner | SCM (239:00)
- GitLab- Session 1 - Summary
- Session 2 - SDLC | Shared Runner | Dedicated Runner | AWS Instance As Runner | Pipeline As Code (156:51)
- GitLab- Session 2 - Summary
- Session 3 - Setting up CI/CD pipeline on two runners (215:27)
- GitLab- Session 3 - Summary
- Session 4 - Advance pipeline concepts and Variables (252:17)
- GitLab- Session 4 - Summary
- Session 5 - 20th August - Integration of GitLab and Kubernetes | Set-up Infrastructure | GitOps | Xac | Runner | Kubernetes Cluster | Pre-created Terraform code | Environment | Job Template | (300:11)
- GitLab- Session 5 - Summary
Drive Link
Available in
days
days
after you enroll
Mlops Training
Available in
days
days
after you enroll
- Mlops Introduction (152:53)
- python basics | List and nested list in python | Data analysis and pandas (267:16)
- Numpy Array | Image processing | Image cropping | Data analysis basiccs and linere regression (275:45)
- Regression model concept | Linear regression | Image processing (247:13)
- Linear regression | Python basics | Image processing (250:49)
- Linear regression , Prediction, Ml model, Matplotlib, Sklearn, Seaborn, Loss function, Metrices, Feature elimination (265:37)
- Linear regression | Feature selection | multi linear regression | computer vision (232:48)
- 29. Logistic regression | Lazy learning | K-nearest neighbours algo | Pattern recognizing KNN (115:42)
- 30. Supervised learning | Unsupervised learning | Clustering | Soft clustering ( soft kmeans ) (139:24)
- 31. Feature scaling | Standardization | Normalization | Kmeans | Iterations (136:03)
- Computer vision ( opencv2 ) | Pre-trained model | Haarcascade model | Ip webcam integration | Multi linear regression (263:24)
- Multi linear regression | Feature selection | Feature engineering concept | Categorial variable | Dummy variable | Multi co-linearity, (233:42)
- Dimensity reduction | feature selection | feature extraction | linear regression | p-value and significance level | OLS and backward elimination and wrapper methond (163:54)
- neural learning / Dl | image processsing | OLS | feature selection | gretl | graphs | object detection | face recognization | docker | linux basics (271:08)
- Tensorflow | Scalar | lazy execution | Eager execution | Perceptron model | Gradient descent (200:01)
- Graph | Tensorflow | DL | Neural Network | Artificial neural network | Activation function | CNN concept (145:55)
- Activation function | DL | Regression | Classification | Feed forward Forward Propogation | Back propagation | Keras (267:57)
- Neural network | Data visualization | graph | folium | Object detection | CNN | YOLO-Setup and Use | Optimizer | Initializer (257:09)
- Data visualization | Classification | Histogram | Graph | Heatmap | Regression | Binary classification (265:34)
- Binary Classification | Model using sigmoid function | Logistic regression | visuals, searborn | graph | bar graph | Discrete variable | frequency distribution table | One hot encoding | Feature Engineering | Missing value resolving using feature engineering | Imputation (123:46)
- Visualization | Exploratory data analysis | One hot label encoding | Nan | Binary class | logistic regression | confusion matrix (118:54)
- Graph | Seaborn | folium | Seaborn | Pandas | Plotly | Cuffling (115:47)
LLMOps Training specialized in DevOps integrated with GenAI
Available in
days
days
after you enroll
- Session 1 - Generative AI | LLM Model | Few shot Prompt |Chain of thought concept |AI Agents |Frame Work |LangChain |OpenAi Play Ground |Lang chain Tools|Provider|Python |Install Lang chain |Create LLm model |Import Agent |Import Tool|Google Search Library |Google API |SerpApi (110:29)
- Summary - Session 1
- Session 2 - AI Agent |Langchain Framework |Tools|SerpAPI|Import Google search |create Env file |Create SerpApi Key|Model + Agent + Tools = Chain|Verbose|Create a Chain of Google search (67:37)
- Summary - Session 2
- Session 3 - Use case of CLI|Agent |Tool|Bash shell|Launch Ec2 instance|install pip|install lang chain model |Create a Python program |Shell tool |Create a Llm model for the CLI |Install AWS commands |Launch Ec2 instance with LLm model (100:13)
- Session - Session 3
- 4. LLMOps - Langchain Training By Mr. Vimal Daga on 16th October_GMT20241016-133629 (112:52)
Example Image with Text
Use this Image with Text block to balance out your text content with a complementary visual to strengthen messaging and help your students connect with your product, course, or coaching. You can introduce yourself with a profile picture and author bio, showcase a student testimonial with their smiling face, or highlight an experience with a screenshot.
Example Featured Products
Showcase other available courses, bundles, and coaching products you’re selling with the Featured Products block to provide alternatives to visitors who may not be interested in this specific product.
EXAMPLE
Check your inbox to confirm your subscription