Learn & gain practical knowledge in data preprocessing, transformation, reduction, and manipulation techniques essential for machine learning. Explore feature scaling, categorical data conversion, and foundational concepts of machine learning, including linear and logistic regression models.
- Data Preprocessing
- Data Transformation
- Data Reduction
- Data Wrangling and Manipulation for Machine Learning
- Feature Scaling
- Categorical Conversions
- Introduction to Machine Learning
- Simple Linear Regression
- Machine Learning Basics
- Multiple Linear Regression
- Ridge and Lasso Regression
- Logistic Regression
Get an introduction to AI and TensorFlow, covering its core functions, computational processes, and automatic differentiation. Understand the fundamentals of neural networks along with activation and loss functions.
- Introduction to AI
- Introduction to TensorFlow
- Working of TensorFlow
- TensorFlow Calculations
- Automatic Differentiation
- Introduction to Neural Networks
- Introduction to Activation Functions
- Activation Functions
- Loss Functions
Gain a clear understanding of artificial neural networks, gradient descent, and stochastic gradient descent. Explore evaluation techniques for both classification and regression in ANN, along with concepts like early stopping and perceptron models.
- Artificial Neural Networks (ANN)
- Gradient Descent
- Stochastic Gradient Descent
- Evaluation Method – Classification
- Regression in ANN
- Classification in ANN
- Early Stopping
- Perceptron Models
Explore the core concepts of Convolutional Neural Networks (CNNs), focusing on how to build and train models for image classification with Python.
- Introduction to CNN
- Building CNN
- CNN Models
- Image Classification in CNN
- CNN Model Building in Python
Explore the world of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Gain practical knowledge of text preprocessing, bag-of-words, TF-IDF, sentiment analysis, and POS tagging. Delve into sequential data modeling with RNNs and LSTMs, while addressing challenges like the vanishing gradient problem for advanced text analytics.
- Handling Missing Values
- Data Transformation
- Feature Engineering
- Data Inspection
Learn the fundamentals of computer vision, covering image representation, edge detection, thresholding, and techniques for face and object detection using Haar Cascade Classifier and SSD. Explore practical video analysis methods with Python.
- Introduction to Computer Vision
- Image Representation
- Black and White Conversion
- Working of Computer Vision
- Edge Detection Filters
- Simple Thresholding and Adaptive
- Thresholding
- Face and Eye Detection using Haar
- Cascade Classifier
- Object Detection using SSD
- Video Analysis in Python
