AI and Machine Learning
Key features include automation, where repetitive tasks are handled efficiently; predictive analytics, which helps forecast outcomes based on historical data; and natural language processing (NLP), enabling machines to understand and respond to human language.
Course Detail
In this course, you will learn the fundamentals of AI and Machine Learning, including essential algorithms, data preprocessing, model evaluation, and real-world applications. You will work with popular programming languages like Python, and explore key libraries and frameworks such as TensorFlow, Keras, and Scikit-learn to build your own machine learning models.
Course Features
- Hands-On Learning
- Expert Instructors
- Comprehensive Curriculum
- Flexible Learning
- Practical Projects
- Machine Learning Algorithms
- Deep Learning Techniques
- Data Science Tools
- Model Evaluation & Tuning
Dive into the world of Artificial Intelligence and Machine Learning with our comprehensive course designed for learners at all levels. Whether you’re looking to start a career in AI or enhance your existing knowledge, this course will equip you with the tools and techniques needed to build intelligent systems and leverage data for decision-making.
- 6 weeks of course
- Recorded Sessions with 1 year access
- Access our classes on Web and Mobile
- Expert Instructors
- Interactive Learning Tools
Course Content
- Introduction to AIML Introduction Artificial Intelligence History of Artificial Intelligence ML Introduction
- ML In Artificial Intelligence ML Tools and Packages Applications of ML
- DL Introduction
- DL In Artificial Intelligence DL Tools & Packages Applications of DL
- Python for AI and ML
- Python Basics
- Python Packages
- Working with NUMPY
- Working with Pandas
- Introduction to Data Visualization
- Introduction to Matplotlib and Seaborn
- Basic Plotting with Matplotlib and Seaborn
- Exploring Tensorflow and keras
- Assignment & Quiz-1
- Data Wrangling Techniques Introduction to Data preprocessing Importing the Dataset
- Handling Missing data – Mean, Median, Mode
- Working with categorical Data – One Hot Encoding, Label Encoding Finding Outliers
- Handling Outliers using Quantile Method, Box Plot
- Transformation Methods (Log, Reciprocal, Square root, Exponential, Box Cox)
- Feature Scaling
- Splitting the data into Train and Test set Feature Selection – Forward Selection, Backward Elimination
- Supervised Learning – Regression Introduction to Regression Regression and its types
- Linear Regression
- Decision Tree Classification Random Forest Classification
- K-nearest Neighbors Naïve-Bayes
- Support Vector Machine Ensembling Techniques
- ML With Tensorflow
- Introduction to tensorflow library
- Random Forest Model
- Gradient Boosted Tree Model
- Model Evaluation Metrics
- Regression Evaluation Metrics
- MAE
- MSE
- R Squared
- RMSE
- Classification metrics
- Confusion Metrics
- Accuracy
- Precision
- Recall F1 Score
- AUC ROC Curves
- Model Hyper-parameter Optimization
- Handling Imbalanced Data
- Oversampling
- Undersampling Ensembling Techniques SMOTE
- Hyper-parameter tuning
- Grid Search
- Randomize Search
- Assignment & Quiz-3
- Introduction to Flask
- Flask Basics
- Fetching values from templates and performing some arithmetic calc.
- Introduction to Neural Network
- What is an ANN Forward propagation
- Activation function Backward propagation
- Gradient Descent
- Introduction to CNN
- Data Augmentation Conv-layers
- Fully connected layer Evaluating CNN Model
- Transfer Learning Introduction to Transfer Learning VGG-16
- Inception
- Xception ResNet 50
- Evaluating Transfer Learning Model
- DL Flask Local Deployment
- Working with Flask framework
- Building an application with Flask Framework Integrating Deep learning & Transfer Learning model with Web Application
Payment & Refund Policy:
- Non-Refundable Fees: Once paid, course fees are strictly non-refundable under any circumstances.
- No Course Transfers: Enrollment in a specific course is final. No transfers or adjustments to another course will be allowed after payment.
- Strictly Personal Use: Course materials, including videos, notes, and resources, are for personal use only.
- No Unauthorized Sharing: Sharing, distributing, or reselling course content in any form is strictly prohibited.
- Legal Action: If we find that any course content, including videos, has been shared without authorization, legal action will be taken against the individual involved.