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
Machine Learning
Available in
days
days
after you enroll
- 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 - 16 - 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 - 17 - 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
- Day 14 - 29th June-Handling null data | one hot encoding and label encoding | testing model (94:31)
- Session - 14 - Summary
- Day 15 - 30th June-Confusion matrix introduction | mathematical function (100:13)
- Session - 15 - Summary
- Day 16 - 3rd July-Neural network | implementation of keras and tensorflow | training model (108:01)
- Session - 16 - Summary
Deep Learning Sessions
Available in
days
days
after you enroll
- 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 4 - 10th July-Installation of mediapipe and cvzone| Hand Detector Class |Each Finger Predefined Point (138:54)
- Session 4 - Summary
- Session 5 - 11th July- Local Binary Histogram Pattern |Program Flow |Test info on face area and Images (71:20)
- Session 5 - Summary
- Session 6 - 12th July-Convolutional Neural Network | Feature Extraction | Architecture and working of CNN | Layer types VIDEO (75:22)
- Session 6 - 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 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 part 1 (93:12)
- Session 11 - 22nd July-project of RNN google stock price part 2 (67:15)
Exttra Sessions
Available in
days
days
after you enroll
Python Sessions
Available in
days
days
after you enroll
- Python - 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
- Python - 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
- Python - 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
- Python - 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
- Python - 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
- Python - 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
- Python - 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
- Python - 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
- Python - 21st June-jazbaa (87:46)
- Python - 22nd June-__dict__ | object. |underscore or double underscore. | Python interpreter version manipulation. | dir() function | Constructor Method | __init__ (96:42)
- Session - 10 - Summary
- Python - 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 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 19 - 12th July-Server| Socket programming. | Networking program | IP + port Number means | Bind function | netstat -tnip command. | client (97:18)
- Session 20 - 13th July-server side code | client side program | network programming | conn variable | conn.recv | os.system (67:16)
- 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 (88:37)
- Session 24 - 22nd July-summer guidance (71:24)
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 (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 8 - 19th Sept-Pipeline | Tokenizer |Analyze pipe using Spacy| add pipe |@language (95:46)
- Session 9 - 20th Sept-Spacy Usage | Creating And Adding Custom Attributes| Register Token Extension (79:25)
- Session 10 - 22nd Sept_Practice Session (49:17)
- Session 11 - 28th Sept-use case of text classification|stemming or lemma| (96:10)
- Session 12 - 4th Oct-text classification|TF-IDF|classification model| (102:38)
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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