Artificial Intelligence
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and perform tasks that typically require human intelligence.
About Artificial Intelligence
Artificial Intelligence (AI) is an Intelligence given to particular bot to perform particular task. It is as similar as teaching new born baby until he becomes adult about Does and Don’ts you want him to do.Â
Similarly, we train our machine / code in order to get particular output to perform particular task. It is one of the trending field for career.Â
In this Tech Cryptor’s Artificial Intelligence course you will learn about coding in Python & R language . We will be using PyCharm & RStudio for coding, which are one of the software’s used at professional level. We will be performing different algorithms by practical coding, also we will be making different projects based on ML.Â
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Learn the fundamentals of Machine Learning and understand how intelligent systems learn from data.
Explore the complete Machine Learning workflow, including data collection, data preprocessing, feature engineering, model training, model evaluation, testing, and prediction.
Understand essential Machine Learning concepts, terminologies, and real-world applications.
Learn the differences between Supervised Learning, Unsupervised Learning, and Reinforcement Learning, along with their practical use cases.
Learn to collect, clean, and prepare datasets for Machine Learning models.
Explore data preprocessing techniques including data cleaning, handling missing values, encoding categorical data, and feature scaling.
Understand feature selection and feature engineering techniques to improve model performance and prediction accuracy.
Learn to visualize datasets and model outputs using various data visualization techniques.
Learn to work with the most widely used Python libraries for Machine Learning and Data Science.
Learn to process data, perform analysis, build Machine Learning models, and visualize results using these libraries.
Learn to understand the concepts, mathematical intuition, implementation, and practical applications of regression algorithms using Python.
Learn the fundamentals of Simple Linear Regression.
Understand how numerical values can be predicted from a single independent variable.
Explore practical implementation using real-world datasets.
Learn to interpret regression results and predictions.
Yes. Tech Cryptors Training Academy is ISO 9001:2015 CERTIFIED COMPANY under IAF. CERTIFICATION will have the significance of the above.
We conduct this course in BOTH online and offline modes. You can choose the mode suitable for you, even you can SWITCH modes for a couple of lectures in case of any personal issues and emergencies.
In one batch we allow MAXIMUM 4 students Because we believe that to have a better understanding and to excel in this course PERSONAL ATTENTION is needed.
We have new batches starting every 15 days, But you should register asap for your desired date because our batches are filling fast. Batch timings are also kept AS PER YOUR TIME CONVENIENCE, we don’t have any rigid preassigned slots.
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Learn to build prediction models using multiple independent variables.
Understand how multiple features influence prediction accuracy.
Implement Multiple Linear Regression using Python.
Learn how Polynomial Regression models nonlinear relationships.
Explore curve fitting and prediction using polynomial features.
Implement Polynomial Regression with practical datasets.
Learn the concept of Logistic Regression for classification problems.
Understand probability prediction and decision boundaries.
Implement Logistic Regression using Python for binary classification tasks.
Learn to build Machine Learning models that classify and predict categories using real-world datasets.
Learn how Decision Tree algorithms split data and make predictions.
Explore entropy, information gain, and decision boundaries.
Learn the working principles of ensemble learning.
Understand how Random Forest combines multiple Decision Trees for improved accuracy.
Build and evaluate Random Forest models.
Learn how Support Vector Machines classify complex datasets.
Understand hyperplanes, support vectors, and kernel functions.
Implement SVM classification using Python.
Learn how KNN performs classification based on nearest neighboring data points.
Explore distance metrics and value selection of K.
Implement KNN classifiers using practical datasets.
Learn ensemble learning using the Bagging Algorithm.
Understand how multiple models improve prediction performance.
Implement Bagging techniques using Scikit-learn.
Learn to split datasets into training and testing datasets.
Understand model training and testing concepts.
Explore prediction and evaluation techniques.
Learn to evaluate Machine Learning models using: Accuracy Precision Recall Confusion Matrix
Understand model performance analysis and improvement techniques.
Learn the concepts of overfitting and underfitting and methods to improve model generalization.
Learn the core concepts behind Artificial Neural Networks and understand how deep learning models process information.
Learn how biological neurons inspire Artificial Neural Networks.
Understand neurons, weights, bias, forward propagation, and backpropagation.
Explore weight adjustment during model training.
Learn about activation functions like ReLU and Softmax
Understand the deep learning training process and architecture.
Learn to build fully connected Artificial Neural Networks for prediction and classification.
Learn the concept behind Artificial Neural Networks.
Practically code Artificial Neural Networks using Python.
Build a fully connected ANN to classify handwritten digits from the MNIST dataset.
Understand pattern recognition using neural networks.
Implement training loops and monitor model performance using loss and accuracy metrics.
Visualize model predictions and misclassified images.
Learn to build powerful image recognition models using Convolutional Neural Networks.
Understand how CNNs detect spatial hierarchies in images.
Learn convolution layers, pooling layers, filter kernels, stride, and padding.
Build and train CNN models using the MNIST dataset.
Learn feature extraction techniques used in computer vision.
Explore dropout, batch normalization, and data augmentation to improve model accuracy.
Visualize feature maps and understand what the CNN model learns.
Learn the concepts and workflow of Natural Language Processing for text understanding.
Learn the fundamentals of Natural Language Processing.
Explore text preprocessing techniques including: Tokenization Padding Stop-word Removal Stemming Word Embeddings.
Understand how textual data is prepared for Deep Learning models.
Learn sequence modeling using Recurrent Neural Networks.
Understand how RNN models process sequential data.
Build an RNN model for positive and negative sentiment classification.
Train and evaluate models using the IMDB Movie Reviews dataset.
Learn text prediction using sequential neural networks.
Learn advanced sequence learning using LSTM networks.
Explore how LSTM overcomes the limitations of traditional RNN models.
Build LSTM models for sequential data processing.
Train LSTM models for text prediction and language understanding.
Learn to build practical Artificial Intelligence projects by implementing Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and Voice Automation concepts.
Build a handwritten digit recognition system using Deep Learning.
Train models using the MNIST dataset.
Predict handwritten digits with high accuracy.
Learn to build an Artificial Intelligence project for converting images into text.
Implement image processing and text extraction concepts.
Learn to build your own AI-powered JARVIS Chat Assistant.
Develop a chatbot that listens to user commands.
Perform operations such as opening Facebook, YouTube, and executing various system commands using voice interaction.
Learn to control your computer using voice commands.
Implement voice-based automation using Artificial Intelligence.
Perform different system operations through spoken commands.
Learn the basic logic behind Graphical User Interface (GUI) development using Python.
Build an interactive Tic-Tac-Toe game with GUI.
Implement a rule-based Artificial Intelligence auto-player.
Understand decision-making logic used in simple AI game development.
Learn different regression algorithms using Python.
Practically implement Machine Learning algorithms to understand their working logic.
Explore classification algorithms through hands-on coding.
Learn the concepts behind Neural Networks and Deep Learning.
Build Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN).
Train, evaluate, and improve Machine Learning and Deep Learning models.
Visualize datasets, predictions, and model performance.
Explore Natural Language Processing techniques for text analysis.
Build RNN and LSTM models using real-world datasets.
Develop practical Artificial Intelligence applications including digit recognition, image-to-text conversion, chatbots, voice assistants, and AI-powered games.
Gain hands-on experience by implementing every major concept using Python and real datasets.
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Original Price
₹30,999 /Course
- Max 4 Students per batch
- 1 to 1 Interaction
- Online & Offline mode
- only Video Training
- limited sessions
- Big batches
Current Price
₹21,999 /Course
- Industry level content
- 100% Practical Training
- Experienced Trainers
- Certification for Course
- Corporate Level Examples
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Reattempt
₹0/Course
- Free Reattempting of Course
- Long Term Support after Completion
- Free Doubt Solving Sessions / Chats
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Original Price
₹26,999 /Course
- Max 4 Students per batch
- 1 to 1 Interaction
- Online & Offline mode
- only Video Training
- limited sessions
- Big batches
Current Price
₹19,999 /Course
- Industry level content
- 100% Practical Training
- Experienced Trainers
- Certification for Course
- Corporate Level Examples
- Many other discounts ASK NOW
Reattempt
₹0/Course
- Free Reattempting of Course
- Long Term Support after Completion
- Free Doubt Solving Sessions / Chats
- Discount for Next Courses
- Real-Time Tasks
- Suggestions & Carrer Counselling