Certified Machine Learning Master (CMLM)

1. Introduction to Python Programming

• Overview of Python
• History of Python
• Python Basics: variables, identifiers, indentation
• Python data structures (dictionary, list, string, sets, and tuples)
• Statements in Python (conditional, iterative, jump)
• OOPS concepts
• Exception Handling
• Regular Expression

2. Introduction to various packages and related functions

• Numpy, Pandas and Matplotlib
• Pandas Module
• Series
• Data Frames
• Numpy Module
• Numpy arrays
• Numpy operations
• Matplotlib module
• Plotting information
• Bar Charts and Histogram
• Box and Whisker Plots
• Heatmap
• Scatter Plots

3. Data Wrangling using Python

• NumPy: Arrays
• Data Operations (Selection, Append, Concat, Joins)
• Univariate Analysis
• Multivariate Analysis
• Handling Missing Values
• Handling Outliers

4. Introduction to Machine Learning with Python

• What is Machine Learning?
• Introduction to Machine Learning
• Types of Machine Learning
• Basic Probability required for Machine Learning
• Linear Algebra required for Machine Learning

5. Supervised Learning: Regression

• Simple Linear Regression
• Multiple Linear Regression
• Assumptions of Linear Regression
• Polynomial Regression
• R2 and RMSE

6. Supervised Learning: Classification

• Logistic Regression
• Decision Trees
• Random Forests
• SVM
• Naïve Bayes
• Confusion Matrix

7. Dimensionality Reduction

• PCA
• Factor Analysis
• LDA

8. Unsupervised Learning: Clustering

• Types of Clustering
• K-means Clustering
• Agglomerative Clustering

9. Additional Performance Evaluation and Model Selection

• AUC / ROC
• Silhouette coefficient
• Cross-Validation
• Bagging
• Boosting
• Bias v/s Variance

10. Recommendation Engines

• Need for recommendation engines
• Types of Recommendation Engines
• Content-Based
• Collaborative Filtering

11. Association Rules Mining

• What are Association Rules?
• Association Rule Parameters
• Apriori Algorithm
• Market Basket Analysis

12. Time Series Analysis

• What is Time Series Analysis?
• Importance of TSA
• Understanding Time Series Data
• ARIMA analysis

13. Reinforcement Learning

• Understanding Reinforcement Learning
• Algorithms associated with RL
• Q-Learning Model
• Introduction to Artificial Intelligence

14. Artificial Neural Networks and Introduction to Deep Learning

• History of Neural Network
• Perceptron
• Forward Propagation
• Introduction to Deep Learning