Duration
2 months
Mode
Online/Offline
Batch Size
10
Modules
1
LMS Access
Yes
Timing
Weekday/Weekends
Introduction
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
You will learn how to use tools such as OpenCV, NumPy and TensorFlow for performing tasks such as data analysis, face recognition and speech recognition.
Goal of Course
- Implementing supervised & un-supervised machine learning algorithms
- Implementing Image Processing
- Using Natural Langauge Processing
- Implementing Deep Learning using Neural Networks
Key Modules
- Machine Learning-Supervised
- Machine Learning-Un-supervised
- Image Processing
- Natural Language Processing with NLTK
- Deep Learning with Python
Machine Learning Fundamentals
- Orientation Of Machine Learning And Industry Use-Cases.
- Different Types Of Machine Learning Techniques.
- Understanding The Sklearn Package For Machine Learning
- Linear Regression
- Logistic Regression
- Naive Bayes
- Advanced Regression
- Support Vector Machine
- Tree Models - Decision Tree, Decision Forest Tree
- K Nearest Neighbors
- Unsupervised learning: Clustering - K Means
- Unsupervised Learning: Principal Component Analysis
Image Processing
- Introduction To Open
- Cv Image Processing Library
- Processing Real-Time Images With Opencv
- Industrial Case Study On Use Case Of Opencv
Natural Language Processing with NLTK
- Introduction To NLP
- Text Tokenization, Chunking, Pos-Tagging Using Nltk.
- Syntactic Parsing In Python
- Entity Recognition From Document
- Text Mining In Python
- Sentiment Analysis
Deep Learning with Python
- Introduction To Neural Networks
- Mathematics Behind Neural Network
- Artificial Neural Networks
- Convolutional Neural Networks
- Convolutional Neural Networks - Industry Applications
- Recurrent Neural Networks
- Recurrent Neural Networks- Industry Applications
Note :you can speak to our team for detailed content and available batch timings.
