Machine Learning Course

Beginner 0(0 Ratings) 7 Students enrolled
Created by Certy Box Last updated Wed, 07-Apr-2021
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Curriculum for this course
170 Lessons 00:00:00 Hours
Introduction to Machine Learning
9 Lessons 00:00:00 Hours
  • Intro to Machine Learning 00:00:00
  • Supervised Learning 00:00:00
  • How to build Models 00:00:00
  • Overfitting 00:00:00
  • Precision & Recall 00:00:00
  • Linear Regression Model 00:00:00
  • Gradient Descent Optimisation Algorithms 00:00:00
  • k-nearest Neighbor 00:00:00
  • Decision Tree Learning 00:00:00
  • Classification vs Regression 00:00:00
  • Regression Trees 00:00:00
  • CART Decision Tree Learning 00:00:00
  • Random Forests 00:00:00
  • Random Forests with Python 00:00:00
  • Logistics Regression Model 00:00:00
  • Linear Regression Model in Python 00:00:00
  • Support Vector Machines 00:00:00
  • Kernel Methods for SVMs 00:00:00
  • Support Vector Machines in Python 00:00:00
  • Naive Bayes Classifiers 00:00:00
  • Naive Bayes Classifiers in python 00:00:00
  • Practice Exercise_Supervised Learning in python 00:00:00
  • Unsupervised Learning 00:00:00
  • Rules Association 00:00:00
  • Apriori in Python 00:00:00
  • Clustering 00:00:00
  • K-means Clustering 00:00:00
  • Clustering in Python 00:00:00
  • Anomaly Detection 00:00:00
  • Anomaly Detection in Python 00:00:00
  • Dimensionality Reduction 00:00:00
  • Principal Component Analysis 00:00:00
  • Dimensionality Reduction in Python 00:00:00
  • Unsupervised Learning in python 00:00:00
  • Neural networks 00:00:00
  • Neural Networks Structure 00:00:00
  • Activation Functions 00:00:00
  • Training Neural Networks 00:00:00
  • Training Neural Networks with backpropagation 00:00:00
  • Batch Learning 00:00:00
  • Tensorflow 00:00:00
  • Setting up Tensorflow 00:00:00
  • Importing data in Tensorflow 00:00:00
  • Building and Training a Single-layer NN in TF 00:00:00
  • Building and Training a Multi-layer NN in TF 00:00:00
  • Practice Exercise_Describe Neural Networks 00:00:00
  • Convolution Neural Networks 00:00:00
  • CNN Architecture 00:00:00
  • Convolution Layers 00:00:00
  • Pooling Layer 00:00:00
  • CNN Training considerations 00:00:00
  • Regularization 00:00:00
  • Convolutional neural network implementation 00:00:00
  • Regularization methods for CNNs 00:00:00
  • Recurrent Neural Networks 00:00:00
  • RNN Types 00:00:00
  • Long short term memory in tensorflow 00:00:00
  • RNN and LSTM for language modelling 00:00:00
  • Practical Exercise CNN in Tensorflow 00:00:00
  • Cross Validation 00:00:00
  • Cross-Validation in Python 00:00:00
  • Metrics for Binary Classification Models 00:00:00
  • Metrics for non binary classification models 00:00:00
  • Classification metrics in Python 00:00:00
  • Metrics for regression models 00:00:00
  • Regression metrics in python 00:00:00
  • AWS Machine Learning 00:00:00
  • Setting up an AWS environment for machine learning 00:00:00
  • Creating a model in AWS 00:00:00
  • Training model & making predictions 00:00:00
  • Describe bias & variance 00:00:00
  • Supervised learning algorithm 00:00:00
  • Bias & Variance 00:00:00
  • Machine learning & deep learning 00:00:00
  • Machine learning and AI correlation 00:00:00
  • Installing python for machine learning 00:00:00
  • benefits of predictive & descriptive analytics 00:00:00
  • nominal, ordinal, interval & ratio data metrics 00:00:00
  • Analytics types & techniques 00:00:00
  • Implementing regression 00:00:00
  • Practice Exercise_Working with Data Frames 00:00:00
  • Working with Classification 00:00:00
  • Unsupervised Learning 00:00:00
  • K-means Clustering 00:00:00
  • Hierarchical clustering 00:00:00
  • Text mining & Recommender systems 00:00:00
  • Text mining & Data assembly 00:00:00
  • Deep & Reinforcement learning concepts 00:00:00
  • Restricted Boltzmann 00:00:00
  • Working with CNN 00:00:00
  • Practical Exercise : Working with data frames & centroids 00:00:00
  • Recurrent neural network 00:00:00
  • Data Sampling 00:00:00
  • Applying PCA 00:00:00
  • Gaussian regression process 00:00:00
  • Linear Model 00:00:00
  • Pre model and Workflow 00:00:00
  • Classification & bayesian ridge 00:00:00
  • Logistic regression using linear method 00:00:00
  • Linear regression modelling 00:00:00
  • Practical Exercise_Working with linear regression model 00:00:00
  • Least absolute shrinkage 00:00:00
  • Bayesian ridge regression using scikit learn 00:00:00
  • Data Classification 00:00:00
  • Decision Tree Classification 00:00:00
  • Vector machine using scikit-learn 00:00:00
  • Document classification and naive bayes 00:00:00
  • Post Model Vallidation 00:00:00
  • Using shufflesplit 00:00:00
  • Brute force grid search 00:00:00
  • Practical Exercise_Working with decision tree classifiers 00:00:00
  • Fake Estimator 00:00:00
  • Robotic Process Automation 00:00:00
  • RPA Framework 00:00:00
  • Task Scheduler 00:00:00
  • Manipulate Images 00:00:00
  • File Operation Automation 00:00:00
  • UiPath Fundamentals 00:00:00
  • Implement RPA using UiPath 00:00:00
  • Practical Exercise_Working with image filter 00:00:00
  • ML Algorithm Types 00:00:00
  • Supervised & Unsupervised Learning 00:00:00
  • K-means Cluster 00:00:00
  • KNN implementation 00:00:00
  • Decision tree & Random Forest 00:00:00
  • Linear Regression Analysis 00:00:00
  • Gradient Boosting Algorithms 00:00:00
  • Logistics Regression 00:00:00
  • Probabilistic Classifier 00:00:00
  • Naive bayes Classifier 00:00:00
  • Practical Exercise : Implementing ML Algos 00:00:00
  • Multilayer Networks & Computation Graphs 00:00:00
  • Implementing Multilayer Network 00:00:00
  • Intro to NLP 00:00:00
  • Components of NLP 00:00:00
  • Language & Sentence 00:00:00
  • Tokenizer & Name Finder 00:00:00
  • Detecting parts of speech 00:00:00
  • Classifying texts & documents 00:00:00
  • Using Parser to extract relationship 00:00:00
  • Speech Implementation 00:00:00
  • Practical Exercise : Working with NLP Components 00:00:00
  • Neural Network 00:00:00
  • Implement Neural Network 00:00:00
  • Neural Network Types 00:00:00
  • Implementing Hopfield Neural Networks 00:00:00
  • Back Propagation Neural Networks 00:00:00
  • Role of Activation Function 00:00:00
  • Loss Functions & their benefits 00:00:00
  • Implementing Activation & Loss Functions 00:00:00
  • Implementing Hyperparameter 00:00:00
  • Neuroph Java Neural Framework Capabilities 00:00:00
  • Hyperparameter implementation using DL4J 00:00:00
  • Deep Learning 00:00:00
  • Comparing deep learning & graph models 00:00:00
  • Combining Deep Learning & Graph model 00:00:00
  • Deep learning & graph model use cases 00:00:00
  • Working with Neuroph & Neural networks 00:00:00
  • Expert System Tools 00:00:00
  • Working with Jess 00:00:00
  • Defining Rules 00:00:00
  • Supervised learning & Notations 00:00:00
  • Datasets & Training models 00:00:00
  • Outlier Types 00:00:00
  • Feature Search Techniques 00:00:00
  • Principal Component analysis data transformation 00:00:00
  • Clustering Concept 00:00:00
  • Hierachical Clustering 00:00:00
  • Graph Modelling 00:00:00
  • Exercise_Working with datasets and clustering 00:00:00
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About the instructor
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  • 785 Students
  • 152 Courses
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Certy Box
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Includes:
  • 00:00:00 Hours On demand videos
  • 170 Lessons
  • Full lifetime access
  • Access on mobile and tv