Harry Potter
Execute the standard Gradient Descent computation for headway of a model (Regression or Neural)
Execute the standard Logistic Regression model usually used for orchestrating data into twofold classes like pass/misfire, win/lose, alive/dead or sound/crippled.
Execute the standard Decision Tree Class used for describing data into various classes using a tree-like model of decisions and their expected outcomes.
Gather a CNN/LSTM based model to give a caption to the given picture.
Manufacture a plan model to anticipate using a data base of pictures whether a given driver is redirected, ie, informing, on a call, driving safely, etc
Collect a Neural Network based model to predict what the accompanying word will be in a gathering of words/sentences
Develop a general model with the ultimate objective of translation of articulations and pictures starting with one language then onto the next using Artificial Neural Network.
Gather a Neural association based model to portray various sounds using their uncommon spectrogram into classes like Dog Barking, Sirens, Street Music, etc
Gather a classifier model using Naive Bayes estimation to expect the subject of an article present in a paper
Integrate a classifier for describing 10,000 pictures into 10 classes (canine, pony, cat, etc) using the CIFAR-10 Dataset.
Separate the tweets introduced on twitter on expect the assessment of the tweet for instance certain, negative or fair-minded
Build a general model with the ability to expect the facial sensation of a person in an image.
Develop Facial Emotion Recognition, Distracted Driver Detection activities and astonish determination delegates to get extraordinary positions
Show
Introduction to Machine Learning, Supervised Learning, Steps for Supervised getting Loading Boston Dataset, Training an Algorithm
Introduction TO LINEAR REGRESSION
Introduction to Linear Regression, Optimal Coefficients, Cost work, Coefficient of Determination, Analysis of Linear Regression using hoax Data, Linear Regression Intuition
MULTIVARIABLE REGRESSION AND GRADIENT DESCENT
Customary Gradient Descent, Learning Rate, Complexity Analysis of Normal Equation Linear Regression, How to find More Complex Boundaries, Variations of Gradient Descent
Incline Descent
Key REGRESSION
Managing Classification Problems, Logistic Regression, Cost Function, Finding Optimal Values, Solving Derivatives, Multiclass Logistic Regression, Finding Complex Boundaries and Regularization, Using Logistic Regression from Sklearn
Decision Trees, Decision Trees for Interview call, Building Decision Trees, Getting to Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features
Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees and Overfitting, Pruning
Decision Tree Implementation
Unpredictable FORESTS
Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Regression using decision Trees and Random Forest, Random Forest in Sklearn
Bayes Theorem, Independence Assumption in Naive Bayes, Probability appraisal for Discrete Values Features, How to manage zero probabilities, Implementation of Naive Bayes, Finding the probability for predictable regarded components, Text Classification using Naive Bayes
PROJECT
Preface to KNN, Feature scaling before KNN, KNN in Sklearn, Cross Validation, Finding Optimal K, Implement KNN, Curse of Dimensionality, Handling Categorical Data, Pros and Cons of KNN
Sense behind SVM, SVM Cost Function, Decision Boundary and the C limit, using SVM from Sklearn, Finding Non Linear Decision Boundary, Choosing Landmark Points, Similarity Functions, How to move to new perspectives, Multi-class Classification, Using Sklearn SVM on Iris, Choosing Parameters using Grid Search, Using Support Vectors to Regression
Head Component Analysis
Sense behind PCA, Applying PCA to 2D data, Applying PCA on 3D data, Math behind PCA, Finding Optimal Number of Features, Magic behind PCA
PCA on Images, PCA on Olevitti Images, Reproducing Images, Eigenfaces, Classification of LFW Images
Cifar10
Customary Language Processing
Using Words as Features, Basics of word dealing with, Stemming, Part of Speech, Lemmatization, Building Feature set, Classification using NLTK Naive Bayes
Using Sklearn classifiers inside NLTK, Countvectorizer, Sklearn Classifiers, N-gram, TF-IDF
Why do we need Neural Networks, Example with Linear Decision Boundary, Finding Non-Linear Decision Boundary, Neural Network Terminology, No of Parameters in Neural Network, Forward and Backward Propagation, Cost Function, How to manage Multiclass request, MLP classifier in sklearn
Forward Propagation, Error Function in Gradient drop, Derivative of Sigmoid Function, Math behind Backpropagation, Implementing a clear Neural Network, Optimizing the code using Vector Operations, Implementing a generally Neural Network.
TensorFlow and Keras
Preamble to TensorFlow, Constants, Session, Variables, Placeholder, MNIST Data, Initialising Weights and Biases, Forward Propagation, Cost Function, Running the Optimiser, How achieves the Optimiser work?, Running Multiple Iterations, Batch Gradient Descent
Preamble to Keras, Flow of code in Keras, Kera Models, Layers, Compiling the model, Fitting Training Data in Keras, Evaluations and Predictions
Convolutional Neural Network
Issue in Handling pictures, Convolution Neural Networks, Stride and Padding, Channels, Pooling Layer, Data Flow in CNN
Plan of CNN, Initializing loads, Forward Propagation in TensorFlow, Convolution and Maxpool Functions, Regularization using Dropout layer, Adding Dropout Layer to the association, Building CNN Keras
Building ML Models for back to back Data, Recurrent Neural Networks, How achieves RNN work, Typical RNN Structures, Airline Data Analysis, Preparing Data for RNN, Setting up the RNN model, Analyzing the Output
Dissipating or Exploiting Gradients, Gated Recurrent Units, Variations of the GRU, LSTM
Preamble to Unsupervised Learning, Introduction to Clustering, Using K-infers for Flat Clustering, KMeans Algorithm, Using KMeans from Sklearn, Implementing Fit and Predict Functions, Implementing K-Means Class
Bit by bit directions to pick Optimal K, Silhouette estimation to pick K, Introduction to K Medoids, K Medoids Algorithm, Introduction to Hierarchical Clustering, Top down/Divisive Approach, Bottom up/Divisive Approach
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