Machine Learning
-
Day 1 - 12th June - Machine Learning Usecases | Dataset | Data point | field | Dependent variable | Independent variable | Linear equation or Linear function | training phase | Error/Epsilon | Simple Linear Regression (102:24)
-
Session - 1 - Summary
-
Day 2 - 13th June - Data Analysis and Data Analytics | Target | artificial intelligence | panda | ‘coefficient’ /or ‘magnitude’ /or ‘order’ /or ‘degree’ | Linear regression function | scikit-learn | fitting | 1D to a 2D array | model coefficient | joblib (105:38)
-
Session - 2 - Summary
-
Day 3 - 14th June - basics of ml | Need of ml model | Process of finding cooficient in supervised learning | Read from CSV file | Approach to create model(programming and visualization) (101:57)
-
Session - 3 - Summary
-
Day 4 - 15th June - linear regression on salarydata | Analysing salary data using sklearn module | Checking co-oficients for model | visualizing the model using matplotlib | Error /cost/residual (85:48)
-
Session - 4 - Summary
-
Day 5 - 16th June - understandig actual value and predicted value|Intro of multi linear regression | Supervised learning | Approach of train and test model | Evaluation of performance of model (103:55)
-
Session - 5 - Summary
-
Day 6 - 17th June - intro of computer vision |Image capturing | Video capture using cv2 | Insight on image,pixels | Cropping photo and video (169:12)
-
Session - 6 - Summary
-
Day 7 - 19th June-brainstorming (145:40)
-
Day 8 - 20th June - absolute error and mean absolute error | Analysis on startup data | Implementation of multi linear regression on startup data (91:19)
-
Session - 8 - Summary
-
Day 9 - 21st June - Categorical variables | dummy variables | one hot encoding | data preprocessing | Independent variables | dummy variable trap (77:32)
-
Session - 9 - Summary
-
Day 10 - 22nd June - Mean absolute error | classification and regression | binary classification | multiclass classification (85:25)
-
Session - 10 - Summary
-
Day 11 - 24th June - live video stream | face recognition (165:23)
-
Session - 11 - Summary
-
Day 12 - 26th June - Regression and classification | logistic regression | implementation on Titanic dataset (92:57)
-
Session - 12 - Summary
-
Day 13 - 27th June - Implementation on Titanic dataset | data imputation (90:15)
-
Session - 13 - Summary
-
Session 14 - 29th June - Handling null data | one hot encoding and label encoding | testing model (94:31)
-
Session - 14 - Summary
-
Session 15 - 30th June - Confusion matrix introduction | mathematical function (100:13)
-
Session - 15 - Summary
-
Session 16 - 3rd July - Neural network | implementation of keras and tensorflow | training model (108:01)
-
Session - 16 - Summary
Deep Learning Sessions
-
Session 1 - 1st July - Human Brain and Neurons | Artificial Neural Network | Real life example of how ANN works (213:38)
-
Session - 1 - Summary
-
Session 2 - 5th July - Churn Modelling | How neural network works? | Working with Churn Dataset | Concat the dummy variables (153:12)
-
Session - 2 - Summary
-
Session 3 - 6th July - Wines (Multi Class Outcome) | Creation of model for wine quality check | Code (107:07)
-
Session - 6th july -Summary
-
Session 4 - 10th July - Installation of mediapipe and cvzone| Hand Detector Class |Each Finger Predefined Point (138:54)
-
Session - 10th july -Summary
-
Session 5 - 11th July - " Local Binary Histogram Pattern |Program Flow |Test info on face area and Images (71:20)
-
Session - 11th july -Summary
-
Session 6 - 12th July - Convolutional Neural Network | Feature Extraction | Architecture and working of CNN | Layer types (75:22)
-
Session - 12th july -Summary
-
Session 7 - 13th July - Cats and Dogs Dataset | Convolutional Neural Network |Why odd-sized kernel is preferred? | Implementation of CNN | Convolution Layer |Polling Layer |Fitting and Testing the model (96:38)
-
Session 7- Summary
-
Session 8 - 17th July-cnn|convolution Layer|Pooling Layer| (113:22)
-
Session 9 - 18th July-recurrent neural network (RNN)|long short-term memory(LSTM)|use case of RNN (120:51)
-
Session 10 - 21st July-neural network|data preprocessing|time series|project of RNN google stock price (93:11)
-
Session 11 - 22nd July-project of RNN google stock price part 2 (67:15)
Rhcsa Sessions
Python Sessions
-
Day 1 - 11th June - "python | Use case python | Feature of python | INstall python | IDE | Install IDE Anaconda | CMD command check Python version | print() fuction | addtion Operator | Variable | List | Slice (173:25)
-
Session - 1 - Summary
-
Day 2 - 12th June - Tuple | CRUD operations | of creating a List | slicing operations in list | Nested list | 2-D list | NumPy | install Numpy | Array | zeros ( ) function | linspace() | Adding, removing, and sorting opration | Shape | flatten() method | reshape() function | ndim() function | (127:31)
-
Session - 2 - Summary
-
Day 3 - 13th June - print() function behavior | TTS (Text-to-speech) | library pyttsx3. | pip list | pyttsx3.+double tab| init() | say() function | runAndWait() function | Dictionary data structure | list inside the dictionary | dictionary opration | (101:20)
-
Session - 3 - Summary
-
Day 4 - 14th June - List operations | assignment operator | Copy module | memory address | 3-D data | single index position | Deep Copy| the nested list,2-D list, or 3-D list. | deepcopy() | Shallow copy | function syntax | Indentation | Print() function Vs return() function (105:39)
-
Session - 4 - Summary
-
Day 5 - 15th June - 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)
-
Session - 5 - Summary
-
Day 6 - 16th June - keyword | membership operator | in keyword | all types of containers: lists, dicts, sets, strings etc | File handling | manipulate file | two categories, text file, and binary file | handle file exceptions | File opening | open () function. | append mode| Reading a file (125:34)
-
Session - 6 - Summary
-
Day 7 - 19th June - File handling | read mode ("r"), | write mode ("w") | append mode ("a") | open() function | tell() method | file position: | Seek() function (92:15)
-
Session - 7 - Summary
-
Day 8 - 20th June - Object Oriented Programming | Classes |data structure. | object | instantiation | data member |member functions | Access Modifiers | Public | Private | Setter and Getter (90:45)
-
Session - 8 - Summary
-
Day 9 - 21st June-jazbaa (87:46)
-
Day 10 - 22nd June - __dict__ | object. |underscore or double underscore. | Python interpreter version manipulation. | dir() function | Constructor Method | __init__ (96:42)
-
Session - 10 - Summary
-
Day 12 - 26th June - organize the data | create , update , operations | syntax for inheriting | parent class | child class | subclass | __init__() function or the constructor | country parameter| super () function (98:20)
-
Session - 12 - Summary
-
Day 13 - 27th June - range function| activation record | stack frame, |Stateful functions. | Yield keyword | generator function. | Functional programming Language | Difference b/w good code and normal code (117:59)
-
Session - 13 - Summary
-
Session 14 - 29th June - CPU time | time complexity | read the RAM | module "sys" | getsizeof() | lINUX free -m | generator functions | square brackets ("[ ]") with parentheses→ ”( )”| list comprehensions | Yield function | Sys module |iterator function | next function (88:35)
-
Session - 14 - Summary
-
Session 15 - 30th June - Open() function | hard disk is located | “With” keyword | “speech_recognition” | “Pyaudio”| text.lower() | Google Recognize API (90:28)
-
Session - 15 - Summary
-
Session 16 - 6th July - Module | function (def) | call function | OS module | if __name__ | "__main__" | infinite loop (85:45)
-
Session 16 - Summary
-
Session 17 -10th July-jazbaa meeting (82:37)
-
Session 18 - 11th July - anonymous function |lambda keyword.| adding two numbers function | modulo operator | modulo operator| filter() function | built-in function | database (70:00)
-
Session 18 - Summary
-
Session 19 - 12th July - Server| Socket programming. | Networking program | IP + port Number means | Bind function | netstat -tnip command. | client (97:18)
-
Session 19 - Summary
-
Session 20 - 13th July - server side code | client side program | network programming | conn variable | conn.recv | os.system (67:16)
-
Session 20 - Summary
-
Session 21 - 17th July-introduction of multi-treading|use case of multi-treading (72:08)
-
Session 22 - 18th July-how to connect python with data base|database connectivity (176:49)
-
Session 23 - 21st July-database connectivity |file handling|open() function|read() method|exception handling (88:36)
-
Session 24 - 22nd July-summer guidance (71:24)
Complete NLP Training
-
Session 1 - 4th Sept - Difference between NLP and nlu | sentiment analysis | implementation of textblob | polarity | subjectivity | objectivity (109:15)
-
Session 1 - 4th Sept - Summary
-
Session 2 - 5th Sept - Implementation on dataset | data handling with pandas | textblob implementation on data (82:44)
-
Session 2 - 5th Sept - Summary
-
Session 3 - 6th Sept - Tokenization | lemmatization | stemming | implementation of nltk (103:26)
-
Session 3 - 6th Sept - Summary
-
Session 4 - 7th Sept - nltk installation | WordNet | Spacy | Automatic Conversion from uppercase to lowercase (113:47)
-
Session 4 - 4th Sept - Summary
-
Session 5 - 11th Sept - Revision Session (66:16)
-
Session 6 - 12th Sept-NLU |introduction of spacy |token ID|read token|mapping| (101:52)
-
Session 7 - 18th Sept - NER | Spacy using rule based matching |Phrase Matcher |Hosting on Local Ip (95:57)
-
Session 7 - 18th Sept - Summary
-
Session 8 - 19th Sept - Pipeline | Tokenizer |Analyze pipe using Spacy| add pipe |@language (95:46)
-
Session 8 - 19th Sept - Summary
-
Session 9 - 20th Sept - Spacy Usage | Creating And Adding Custom Attributes| Register Token Extension (79:25)
-
Session 9 - 20th Sept - Summary
AWS Data Analytics Training
-
Session 1 - 9th May_Data analysis | batch/real time processing/analysis | data/Kinesis data stream | AWS serverless | Partition Key | Hashing | Sharding | log/record/event | Big data | velocity/volumne/Saclable/Throghput | High Scality | Shards | Kinesis | AWS cloudshell | Consumers | Iterator (157:48)
-
Summary Session - 1
-
Session 2 - 10th May - Shard and Sharding | Producer (Amazon Kinesis Data Streams) and Kinesis Producer Library (KPL) | Consumer | Multiple Ways to Write Data in Kinesis | Use Case: Real-time Data Streaming | Streaming | Data Processing at the Agent Side | CloudWatch Integration (134:43)
-
Summary Session - 2
-
Session 3 - 12th May - SDK for Kinesis Data Streams | Web Server Logs | Log Conversion to JSON | Kinesis Data Streams | Event Triggers & Notifications | Lambda as Consumer | Real-Time Data Processing | Sending Logs to Kinesis | Log Storage | Types and Triggers | Data Ingestion into KDS (145:46)
-
Summary Session - 3
-
Session 4 - 16th May - Kinesis data stream via Lambda | S3 triggers|Stream setup with shards | Producer Lambda setup | Runtime | Lambda trigger | Producer & Consumer Lambda | Object data retrieval | CloudWatch log viewing | Shard ID & sq number | Trigger addition | Data processing | CloudWatch (130:11)
-
Summary Session - 4
-
Session 5 - 17th May - Kinesis Data Streams and Data Firehose | Data Retention and Storage | Delivery Streams and Configurations | Buffering in Kinesis Data Firehose | Creating a Delivery Stream | S3 Bucket Creation | Data Ingestion and Delivery | Data Organization and Partitioning (119:26)
-
Summary Session - 5
Extra Sessions
