Pre-Requisite

• Basic to Intermediate Python Coding Skills    
• Strong Mathematical Skills

Introduction to ML

• Application of ML
• Math Review

Data Analysis – NumPy, Pandas

• Handling Missing values of Data
• Mathematical Performance
• Dial with Image
• Get Information of Complete Data

Data Visualization – Matplotlib, Seaborn, Plotly

• Graphically Display of Data by using Different Libraries

Natural Language Processing & Text Mining

Train-Test Split, Generalization

Supervised Learning

Classification Algorithms

• Logistic Regression
• K-Nearest Neighbors (choice of K, Time Complexity Analysis)
• Support Vector Machine (SVM)
• Naive Bayes (Probabilistic Methods)
• Decision Tree Classification
• Random Forest Classification
• Gradient Boosting

Evaluating Machine Learning Classification Models Performance

ROC Curve, Confusion Matrix, Sensitivity, Specificity, Precision

Recall, F1-score, Correlation Coefficient

Overfitting, Underfitting

Gradient Descent Algorithm, Optimizer, Regularization

Loss & Cost Function

Regression Algorithm

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Perception Convergence
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Ensemble Methods Such as Random Forest & Ada Boost

Evaluating Machine Learning Regression Models Performance

Unsupervised Learning

• Clustering
• K-Means Clustering
• Soft K-Means
• Hierarchical Clustering
• Agglomerative Clustering

Model Evaluation & Selection

Feature Selection latent factor Models (PCA)

Time Series Analysis