Machine Learning With Python Training | Programming Training in Virtual

Machine Learning with Python Training Course Outline

Module 1: Introduction to Machine Learning 

• What is Machine Learning?
• Python for Machine Learning 
• AI vs Machine Learning
• Classification of Machine Learning
• Supervised vs Unsupervised Learning
• Reinforcement Learning
• Datasets for ML 
• Popular Sources of ML Datasets
• Kaggle Datasets
• UCI Machine Learning Repository
• Datasets via AWS
• Google’s Datasets Search Engine
• Microsoft Datasets
• Computer Vision Datasets
• Scikit-learn Datasets
• Application of Machine Learning 
• Virtual Process Assistance
• Email Spam and Malware Filtering
• Traffic Prediction
• Image Recognition
• Speech Recognition
• Product Recommendation
• Self-Driving Car
• Detection Online Frauds
• Python libraries for Machine Learning
• Numpy
• Pandas
• Matplotib
• Scikit-learn
• Scipy
• Tensorflow
• Pytorch
• Keras

Module 2: Regression 

• Introduction to Regression 
• Why do we use Regression Analysis?
• ​Regression Analysis-Related Terminologies​
• Types of Regression​
• Linear Regression
• Linear Regression Formula​
• Types of Linear Regression​
• Linear Regression Line​
• Polynomial Regression​
• Non-Linear Regression 
• Model Evaluation Process
• Cross Validation in ML
• Methods Used for Cross-Validation​
• Types of Predictive Model​
• Confusion Matrix​
• Area Under the ROC Curve (AUC-ROC) ​
• ROC Curve​
• AUC Curve​
• Application of AUC-ROC Curve​
• Mean Squared Error (MSE)​
• Root Mean Squared Error (RMSE)​
• K-fold Cross Validation​
• Hands-On Linear Regression ​

Module 3: Classification 

• Introduction to Classification
• Classifier​ 
• K-Nearest Neighbours
• How KNN works? ​
• Decision Tree​
• Why to Use Decision Tree? ​
• Decision Tree Terminologies​
• Decision Tree Steps​
• Advantages and Disadvantages (Decision Tree) ​
• Logistic Regression​
• Logistic Function (Sigmoid Function) ​
• Equation of Logistic Regression​
• Types of Logistic Regression​
• Support Vector Machine (SVM)​
• Why is it called Naive Bayes? ​
• Bayes Theorem​
• Advantages and Disadvantages of NB classifier ​
• Types of Naive Bayes Model​
• Random Forest Classification​
• Why Random Forest ​
• Application of Random Forest Classification​
• Advantages and Disadvantages of RF​
• Hands-On Logistic Regression ​

Module 4: Unsupervised Learning 

• Introduction to Unsupervised Learning 
• Types of Unsupervised Algorithm​
• Advantages and Disadvantages of UL
• Unsupervised Learning Algorithms​
• K-Means Clustering 
• Steps for K-means Clustering​
• Elbow Method​
• Hierarchical Clustering ​
• Why Hierarchical Clustering ​
• Density Based Clustering (DBSCAN)​
• Apriori Algorithm​
• Components of Apriori Algorithm​
• Hands-On Clustering ​

Module 5: Dimensionality Reduction 

• Dimensionality Reduction
• Need of Dimensionality Reduction
• Types of Dimensionality Reduction
• Principal Component Analysis (PCA)
• Steps for PCA Algorithm
• What is Variance?
• What is Covariance?
• What is Correlation?
• Application of PCA
• What is P-Value?
• Hypothesis Testing
• Hypothesis in Statistics
• Critical Values
• Z Test
• Chi-Square Test
• ANOVA
• Normal Distribution
• Statistical Significance
• Errors in P-value
• Linear Discriminant Analysis (LDA)
• Working of Linear Discriminant Analysis
• How to Prepare Data for LDA
• Real World Application of LDA
• Difference Between PCA and LDA
• Overfitting and Underfitting in ML
• How to Avoid the Overfitting in the Model
• Hands-On PCA

Module 6: Deep Learning 

• Introduction to Deep Learning
• Importance of Deep Learning
• Neural Network Architecture
• Neural Network Components
• Neural Network Algorithms
• Convolutional Neural Networks (CNNs)
• Long Short Memory Network (LSTMs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFN)
• Multilayer Perceptrons (MLPs)
• Self-Organising Maps (SOMs)
• Deep Belief Networks (DBNs)
• Restricted Boltzmann Machine (RBMs)
• Artificial Neural Networks (ANNs)
• Feed Forward Neural Network
• Autoencoders
• MNIST
• Deep Learning Applications