Python (ML and AI)
Machine learning and AI using Python involve using Python libraries such as scikit-learn and TensorFlow to create algorithms that can make predictions.
Python ML and AI
Master the exciting domains of Python Machine Learning and Artificial Intelligence with Tech Cryptors Training Academy’s (ML and AI) course for professionals and enthusiasts in Mumbai, India.
This Tech Cryptors Training Academy Python ML and AI program offers a deep dive into data analysis, predictive modeling, and intelligent algorithms using Python. Explore popular libraries like TensorFlow and scikit-learn to create and deploy robust machine learning models. Gain practical insights into artificial intelligence fundamentals and learn how to apply them to solve real-world challenges. With expert guidance, hands-on exercises, and industry-relevant projects, this course empowers you to thrive in the dynamic field of ML and AI using Python.
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Overview of Python programming language, its applications in software development, data science, machine learning, and artificial intelligence. Understanding Python environment and industry use cases.
Installation of Python and PyCharm IDE. Understanding project setup, file structure, and working with the Python development environment.
Understanding Python syntax, indentation, comments, variables, naming conventions, and input/output operations used in Python programming.
Learning Python data types such as integers, floats, strings, and booleans. Understanding type casting and working with arithmetic, relational, logical, and assignment operators.
Working with decision-making statements including if, if-else, nested if statements, and loops such as for loop, while loop, and nested loops.
Understanding string operations, string manipulation techniques, and taking user input in different data formats.
Learning lists, tuples, dictionaries, and their functionalities to store and manage structured data efficiently.
Creating reusable functions, lambda functions, function arguments, and learning exception handling techniques for error-free programming.
Working with built-in modules such as math and os modules. Learning file handling operations including creating, reading, writing, and updating files.
Understanding classes, objects, inheritance, polymorphism, encapsulation, and abstraction to build scalable Python applications.
Yes. Tech Cryptors Training Academy is an ISO 9001:2015 CERTIFIED COMPANY under IAF. Also, we are in collaboration with Shastra INDIAN INSTITUTES TECHNOLOGY Madras. 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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Overview of machine learning concepts, types of machine learning, and understanding important machine learning terminologies and workflow.
Learning popular classification algorithms including: Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Bagging Algorithm
Understanding and implementing regression algorithms including: Simple Linear Regression, Multiple Linear Regression, Logistic Regression, Polynomial Regression
Understanding neural networks, neurons, weights, bias, backpropagation, and activation functions such as ReLU and Softmax. Building Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) using real datasets.
Introduction to Natural Language Processing including text preprocessing, RNN, and LSTM models. Building real-world AI projects such as digit recognition, chatbot, voice assistant (JARVIS), image-to-text converter, and AI-based Tic-Tac-Toe game with GUI.
Understanding the complete machine learning workflow including data collection, preprocessing, model training, evaluation, and prediction.
Learning data preprocessing techniques such as data cleaning, handling missing values, and feature scaling.
Understanding the difference between supervised learning, unsupervised learning, and reinforcement learning in machine learning.
Implementing regression models to predict numerical values using real-world datasets.
Building classification models to categorize and predict outcomes based on input data.
Evaluating machine learning models using accuracy, precision, recall, and confusion matrix techniques.
Understanding model training and testing concepts including training datasets and testing datasets.
Learning the concept of overfitting and underfitting and techniques to improve model performance.
Working with Python libraries used in machine learning such as NumPy, Pandas, Matplotlib, and Scikit-learn.
Understanding feature selection and feature engineering to improve machine learning model accuracy.
Visualizing datasets and model results using data visualization techniques.
Building and training Artificial Neural Networks (ANN) for pattern recognition and prediction tasks.
Implementing Convolutional Neural Networks (CNN) for image recognition and computer vision tasks.
Understanding deep learning architecture and training process for complex AI models.
Building Natural Language Processing (NLP) applications for text analysis and language understanding.
Understanding text preprocessing techniques such as tokenization, stop-word removal, and stemming.
Training RNN and LSTM models for sequential data processing and text prediction.
Developing AI-powered real-world applications such as chatbots, voice assistants, and image recognition systems.
Implementing digit recognition systems using deep learning models and image datasets.
Creating interactive AI projects including AI-based games and intelligent automation systems.
Click Here to view a complete step-by-step guide on how to install Python
Click Here to view a complete step-by-step guide on how to install PyCharm
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