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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
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 | content type | backend code (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. opencv | numpy array | crop face | object | computer vision | model | haar cascade | haar cascade face director model | Cascade classifier | detect multi scale function | 2D | coordination | face recognition | not keyword | None keyword (212:48)
- Summary
- 11. cvzone | Tzdata | Cv2 | hand tracking | hand Detector | Find hand | hand landmarks | import os | if else program | pose tracking (215:47)
- Summary
- 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. module | function | import keyword | py extensions | dir function | main module | multiple lines string | polymorphism | + keyword | doc string | from keyword | del keyword | module path | lambda function | anonymous function | call back function | higher order function | filter function (109:43)
- SUmmary
- 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
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 | values keyword | convert 1D to 2D | coefficient | bias concept | visualization technique | data point | historical data | slope | best fit line | simple linear regression | EDA technique | Matplotlib | 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
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 | (427:16)
- 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 (109:15)
- Session 2 - 5th Sept-Implementation on dataset | data handling with pandas | textblob implementation on data (82:44)
- Session 3 - 6th Sept-Tokenization | lemmatization | stemming | implementation of nltk (103:26)
- Session 4 - 7th Sept-nltk installation | WordNet | Spacy | Automatic Conversion from uppercase to lowercase (113:47)
- Session 5 - 11th Sept-Revision Session (132:32)
- Session 6 - 12th Sept-NLU |introduction of spacy |token ID|read token|mapping| (101:52)
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 | Open AI | 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 (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 (239:00)
- Summary 3
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
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