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Specialization in DataScience Training by Mr. Vimal Daga
Python Training
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)
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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)
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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)
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11. cvzone | Tzdata | Cv2 | hand tracking | hand Detector | Find hand | hand landmarks | import os | if else program | pose tracking (215:47)
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12. Class | Object | Memory | RAM | Variable | Function (149:02)
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13. Variable | Function | Object | Class | Keyword | Get method | Set method | Custom data structure | List | Constructor (72:36)
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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
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
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)
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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)
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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)
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11. What is deep learning | Pattern | What is neuron | Input layer | Hidden layer | Output layer | Activation function | Feedforward neural network (121:19)
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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
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)
Deep Learning Sessions
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
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
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
14. Creating model for churn modelling | Feature engineering on churn dataset | Multi-layer perceptron | Kernel initialiser | Glorot uniform initialiser
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