Course Code and Content for Data Science Batch of Dec 2019
- Introduction to datatypes : int, float, boolean
- Iterables, Statements
- Basics of file handling, accessing web urls using "requests" module.
- Introduction to functions, map, lambda, filter
- Brief intro to Lists and Tuples
- Basics of using Markdown
- Introduction to Dictionary and Sets
- Lists functions : copy, append, extend, count, index, pop, remove
- Tuple functions : append, immutability, copy, hashability
- Markov Chains
- Introduction to numpy liberary : size, shape, dtype, array, full, reshape, ones, zeroes
- Concepts of numpy and matplotlib
- Introduction to PIL liberary
- Editing images with PIL
- Overlaying images
- Intoduction to Object Oriented Programming : Human/Hitman classes
- Accessing private members or methods of a class
- Inheritance in OOPs
- Introduction to Stacks
- Stack functions, Dynamic Stacks
- Introduction to Queues
- Circular Queues
- Stack using Queues : Add efficient, Remove efficient
- Dynamic Queues
- Introduction to bs4
- Processing url links, web scraping using module "BeautifulSoup"
- Introduction to Flask and templates
- Creating and accessing a web form from the local PC
- Introduction to pandas
- Creating Dataframes, converting file to csv/html
- Operations on dataframes
- Building a facebook chatbot part-1
- Building a facebook chatbot part-2 (using pymessenger)
- Introduction to Database and SQL
- Operations on Database using SQL
- SQLAlchemy
- Introduction to Linked Lists
- operations on LL : Insert/Delete at first,last or at a position
- Introduction to Recursion
- Examples of Recursion
- Using recursion to find string subsequences
- Using recursion to find string permutaions
- Building a Maze Game
- Using recursion to return number of string subsequences
- Using recursion to return number of string subsequences
- Dice Game using recursion
- Finding LCS in two strings using recursion
- Sorting an Array
- Patterns
- Finding two sub-lists from a lists such that the sum of those two are equal.
- nQueens problem
- Returning list of all possible ways a Rat can get to the Food in an n*n matrix
- Building Sudoku using recursion
- Returning all possible solutions for a Sudoku in a list
- Merge Sort
- Binary Search Tree
- Operations of BST : add, contains, display, height, sum, mirror
- Introduction to AVL Trees
- Introduction to Graph
- Adjacency Graph
- Traversal of Graph using Breadth First ,Depth First
- Search of an element using Breadth First , Depth First
- Adjacency Graph with Connected Components and Bipartite
- Introduction to Adjacency Map Graph
- Dynamic Programming
- Dp on Fibonacci Problem,Knapsack Problem,Dice Problem,Maze problem
- Numpy , Pandas and Matplotlib revisited
- Advanced functionality of Numpy,Pandas and Matplotlib
- Introduction to Linear Regression
- Implementation of Custom Linear Regression
- Linear Regression on Non-linearly separable data
- Introduction to Multiple Linear Regression
- Implementation of Multiple Linear Regression
- Closed Form Solution of Linear Regression
- Lowess
- Introduction to Logistic Regression
- Implementation of Logistic Regression(Gradient Ascend algos used)
- Logistic Regression (Dependent Variable contains more than 2 categories)
- Introduction to K-nearest Neighbours
- Custom Knn
- KNN on MNIST handwritten dataset (Lecture By Kunal Kushwaha)
- Face recognition
- Rounding Video frame on another video frame
- Selecting perticular area in video frame and making one of the layer 0
- haarcascade_frontalface detection
- Swapping largest size faces in video Frame
- Detecting faces and storing gray face data in npy format
- Face recognition
- Detecting faces and storing gray face data in npy format for face recognition
- Detecting faces from training faces
- Introduction to kmeans algorithm
- Extracting Dominant colors using Kmeans
- Titanic Data preprocessing
- Entropy explained
- Decison tree algorithm explained
- Entropy and Information gain
- Decison tree implementation
- Random Forest classifier
- Intro to Naive Bayes classifier
- Mushroom Datasets
- Custom Naivebayes classifier
- Natural language processing
- nltk library used
- Tokenization,Corpus,Stopwords,CountVectorizer
- Working with Modi ji ki speech
- Flask Revised
- computational graph with @tf.function
- Linear Regression in Tensorflow
- Logistic Regression in Tensorflow
- Building Neural N/w
- Adding layers to model
- Deep learning notes
- Imdb Movie Review classification
- Adding Dense layer to Sequential model
- Multi layer perceptron
- Handling handdwritten mnist using neural networks
- Deep learning intro notes
- MLP's notes
- Handling handdwritten mnist using neural networks
- Normalisation on Boston Data
- Performing EDA on Housing Price Data
- Correlation term discussed
- Reducing attributes which have high correlation
- Autoencoders Explained(Mnist Data)
- Principal component Analysis
- PCA on Mnist Data
- Intro to Convolutional neural networks
- Max Pooling,Padding,Pooling
- CNN notes(Blur Filter,Edge Filter,Activation Map,Mnist classification using cnn)
- CNN case studies(Alexnet,Zfnet,Vgg,Google Inception Module)
- Image Data generetor,ImageData augmentation,Pipeline
- Transfer Learning
- Transfer Learning Notes
- Google word2vec Model
- Finding Similiar colors using euclidean distance
- Intro to RCNN
- Classifying gender with names using embedding layer
- Movie Rating Prediction
- Reinforcement Learning
- CartPole
- Intro to Genetics Algo(Using Population examples)
- Genereting similiar images
- Support vector machine explained
- Image Captioning(small flickr dataset)
- Lstm model used
- Scrapping and Crawling revised
- Cb Team member photo scrapped
- Sql Revised
- Generetive adverserial networks
- Time Series Analysis