Artificial Intelligence Course in Bangladesh

For those looking to grow in the field, the Artificial Intelligence Course in Bangladesh is a cutting-edge training program. You will be an expert from this Artificial Intelligence Course with actual knowledge in the essentials, including Python,  Data Science, Machine learning, Deep Learning, Natural Language Processing, and more, taught by top industry professionals.
In Bangladesh, our Artificial intelligence course provides top-notch, hands-on instruction from subject matter experts using interactive teaching techniques. This includes interactive conversations with AI sector experts, live virtual classes, and online training videos. Additionally, candidates have exclusive access to internship facilities, practical industrial projects, and practice exams.
Our certification program for AI will get you ready for success. It aims to assist interested applicants in upgrading their skills and advancing their careers in AI. This course aims to give students the information and abilities needed to pursue a rewarding career in the field of artificial intelligence.

1. 6 Months
2. 40 Modules
3. 9 Projects

Focused Job Role: 

1. Artificial Intelligence Engineer

2. Machine Learning Engineer

3. Data Scientist

4. Computer Vision Consultant

5. Business Intelligence Developer

6. Research Scientist

7. NLP Consultant

Training Key Benefits:

1. Gain expertise with 20+ hands-on exercises
2. Job-oriented real-life industry projects
3. Dedicated mentoring sessions from Industry Expert
4. International Faculty
5. Priority basis Intern Facilities
6. Freelancing support for Market place

Pre-requisites: 

1. Computer, internet with the willingness

Course Overview:

Python Course Module:

1. Dev Environment Setup   
2. Python Core
3. Python Functions
4. Python Object-Oriented Programming   
5. Modules and Packages
6. File Handling TXT, JSON, CSV   
7. Folder and Datetime Handling   
8. Exception Handling
9. Interacting with Web - Requests   
10. Web Scraping - Beautiful Soup   
11. Web Crawling - Scrapy

Detailed Outline ML & DS:

Module 1 Applied Statistics
1. Descriptive Stats
2. Inferential Stats

Module 2  Applied Probability
1. Key Concepts
2. Probability Formulas
3. Conditional Probability
4. Bayes Theorem

Module 3 Scientific Computing
1. N-Dimensional Array Computation - NumPy
2. Scientific Computation 

Module 4 Data Visualization
1. Matplotlib

Module 5  SQL for Data Science
1.MySQL Setup
2. Database
3. Tables
4. Constraints
5. Query and Nested Queries
6. Joins
7. Views

Module 6 Data Science
1. Pandas Series
2. Pandas Data Frame
3. Pandas - Data Cleaning & Pre-processing
4. Exploratory Data Analysis Pandas and Seaborn

Module 7 Machine Learning Supervised Modelling
1. Linear Regression Regression
2. Stochastic Gradient Descent Regression & Classification
3. Logistic Regression Classification
4.Support Vector Machine - Regression & Classification
5.Nave Bayes - Regression & Classification
6.K Nearest Neighbors - Regression & Classification
7.Decision Tree - Regression & Classification
8.Multilayer Perceptron - Regression & Classification

Module 8 Machine Learning Ensemble Learning
1. Bagging
2.Random Forest - Regression & Classification
3. Boosting
4.AdaBoost - Regression & Classification

Module 9 Machine Learning Unsupervised Modelling
1.Clustering
2. K Means
3. Hierarchical
4. DBSCAN
5. Dimensionality Reduction-
6. Principle Component Analysis

Module 10 Image Processing
1. Image Loading, manipulation, and Saving
2. Transformation Resizing, Rescaling, Rotation
3. Colour and Exposure Transformation
4. Feature Extraction HOG

Module 11 Natural Language Processing
1. NLTK
2. Tokenization
3. Stop Words
4. Stemming
5. Lemmatization
6. Vectorization

Module 12 Deploying Models with Flask

Course Module Deep Learning & NLP:

Module 1 Idea of Artificial Intelligence
Module 2 Idea of Deep Learning

1. Idea of Neural Networks
2. Perceptron
3. Training Neural Network
4.Activation Function
5. Understanding Derivatives
6. Forward Propagation
7. Backpropagation
8. Computing Loss
9. Gradient Descent
10. Regularization
11. Optimization
12. Shallow vs Deep Neural Network

Module 3 Tensor Flow
1. Building Shallow Neural Network
2. Regression with Tensor Flow
3. Classification with Tensor Flow
4. Building Deep Neural Network
5. Saving and Loading Model

Module 4 Introduction to Computer Vision
1. Working with OpenCV
2. Image Load and Save
3. Image Representation as Matrix
4. Image Transformations -
5. Colour
6. Shape
7. Scale
8. Rotation
9. Contrast
10. Brightness
11. Blur

Module 5 Convolutional Neural Network (CNN)
1. CNN with Tensor Flow
2. Multiclass Image Classification

Module 6 Sequence Modelling
1. Recurrent Neural Network
2. Long Short-Term Memory
3. Gated Recurrent Unit

Module7 Natural Language Processing Tokenization
1. Stop Words Filtering
2. Stemming
3. Lemmatization
4. Tagging POS
5. Chunking and Chinking
6. Dispersion Plot and Frequency Plot
7. Named Entity Recognition
8. Word Embedding

Module 8 Deploying Models with Flask
2 Mock Interviews

Career Freelancing (Upwork) + LinkedIn + Kaggle

Industry Project:

1) Data Mining Project

2) Time Series - Stock Market Prediction (Data)

3) Crop Disease Prediction (image)

4) Sentiment Analysis (Text)

5) Advanced House Price Prediction

6) Twitter Sentiment Analysis (Text)

7) Fake News Detection (Text)

8) Skin Cancer Detection

9) pneumonia detection from x-ray images

Certificate:

International Collaboration: Data Core
National Collaboration: Jashore University of Science and Technology (JUST)
Membership: BASIS