Lutfor Rahman

Python, Numpy, OpenCV & Tensorflow. What is a ANN What is DNN What is a CNN Basic Activation Functions Basic Optimizers Regressions Project 1,2,3 - Creating ANN to solve Linear Regression Problem Classifications Project 4,5 - on Classification Project 6 - Creating ANN to recognise hand written digits A better understanding of CNN Project 7 - Creating CNN to recognise hand written digits More Activation Functions More Optimizers More on Convolutional Neural Network Project 8, 9, 10 - Fashion Recognition, Objects Detections & Cats & Dogs Classifier Handling Overfitting & Data Augmentation Transfer Learning Project 11, 12, 13 - Multi Classifier using TL Natural Language Processing Project 14, 15, 16 - Projects using NLP Time series, Sequence, Predictions (CNN,RNN, LSTM) Project 17, 18, 19, 20 ---------- And five more bonus projects --------------- 26. Demo question for Exam

Python, Numpy, OpenCV & PyTorch. What is a ANN What is DNN What is a CNN Basic Activation Functions Basic Optimizers Regressions Project 1,2,3 - Creating ANN to solve Linear Regression Problem Classifications Project 4,5 - on Classification Project 6 - Creating ANN to recognise hand written digits A better understanding of CNN Project 7 - Creating CNN to recognise hand written digits More Activation Functions More Optimizers More on Convolutional Neural Network Project 8, 9, 10 - Fashion Recognition, Objects Detections & Cats & Dogs Classifier Handling Overfitting & Data Augmentation Transfer Learning Project 11, 12, 13 - Multi Classifier using TL Natural Language Processing Project 14, 15, 16 - Projects using NLP Time series, Sequence, Predictions (CNN, RNN, LSTM) Project 17, 18, 19, 20 ---------- And five more bonus projects --------------- 26. Demo question for Exam

I will teach you Computer Vision and Deep Learning using OpenCV, Numpy, Keras and Tensorflow / PyTorch for image processing and using Keras and Tensorflow / PyTorch for making deep neural network for object recognition, face recognition and detection. Creating custom dataset and deep neural network / convolutional neural network based on business requirements. First you will get familiar with the concepts and soon you will be engaged in real life computer vision and deep learning project and you will develop project like - Projects Object detection from image Object detection from video Real time object detection / Web Cam & CCTV Face detection from image Face detection from video Real time face detection / Web Cam & CCTV Text Recognition Measure Size of Object Object Counter Face mask Detection Social Distancing checker Adding water mark to Image and video Similar image finder Similar face finder Handwritten Character Recognition Number-Plate Reader Anomaly Detection in CCTV Footage Hand Gesture Recognition Sign Language Food Recognition using Customised Model Age and Gender Estimation Human Pose Estimation Emotion detection system

Developed about 20 mobile apps using Dart/Flutter. Nevertheless, I've developed a course on Flutter titled - Master Flutter - Learn Dart & Flutter by Developing 20 Apps. Learn Google Flutter & Dart by Developing 20 Real Life & Enterprise Apps including UI, App Dashboard and API - at Udemy. https://www.udemy.com/course/master-flutter-learn-dart-flutter-by-developing-20-apps/?couponCode=FLUT-APR-2020

Developed some web apps and APIs for mobile.

You will learn everything from scratch by understanding the data structures, algorithms and solving problems. From variable declaration in your preferred language to object oriented programming and all the data structures and related algorithms. 1. First you will be taught your preferred language if you already don't know. The language will be taught from very beginning, from variable declarations to object oriented. You will be taught Object to Object Relations. Association object relation, Inheritance relation, By directional relation & Self Object Relation. 2. Then you will be taught more about arrays and how it's created. You will learn how to create your own array and implement different popular methods of array yourself. And finally, you will learn algorithms related to arrays. Like - sorting, searching, two pointers, sliding window, Kadane's algorithm and more. And finally, you will learn to solve problems at leetcode, hackerrank & code studio. 2.i. At the middle of section 2, when we will explore arrays, will learn about Big O Notation. The time and space complexity. And we will continue exploring Big O Notation throughout the course. 3. After array is finished, you will learn Stack & Queue. You will learn to build your own Stack & Queue class and implement all the popular methods yourself. And finally you will learn algorithms related to Stack & Queue and solve problems at leetcode, hackerrank & code studio. 4. At this stage, will learn about LinkedLists. We will learn and understand how nodes connect with each other. How we can insert data at the beginning, at the end and at the middle. Reverse a linked list. Remove node from beginning, end and from the middle of the linked list. Remove duplicates and more. 5. It's time to learn recursion. We will learn what is recursion and how it works and solve all the previous problems using recursion. 6. Now it's time to learn Tree & Binary Search Tree. We will learn how to insert data in a tree, how to get and delete data from a binary tree. We will learn In Order Traversal, Pre Order Traversal and Post Order Traversal. We will learn BFS & DFS and more. 7. At this stage, will learn AVL Tree and its implementation. 8. We will learn Heaps and it's implementations. 9. We will learn Trie and it's implementations. 10. And finally, we will learn about Graphs. We will learn what is a directed and undirected grasp. How to insert data in graph and related algorithms. 11. You will be taught algorithms like - Two Pointers, Sliding Window, Linear Search, Binary Search, Ternary Search, Bubble Sort, Merge Sort, Selection Sort, Quick Sort, Cycle Sort, Topological Sort, Redix Sort, Backtracking, Dynamic Programming, Greedy Algorithm, Kadane's algorithm, Depth First Search, Breath First Search, Bit Manipulation, Divide & Conquer and more. Course benefits and schedule: 1. You will learn to crack FAANG/MAANG like interviews. 2. Important notes will be given in a pdf file. 3. You can choose either 2 classes or 3 classes per week and 60 minutes each class. 4. Every session will be recorded while teaching you