Machine Learning Certification Training Using Python

• Discuss Data Acquisition techniques
• List the different types of Data
• Evaluate Input Data
• Explain the Data Wrangling techniques
• Discuss Data Exploration

• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data

• Loading different types of dataset in Python
• Arranging the data
• Plotting the graphs

• Essential Python Revision
• Necessary Machine Learning Python libraries
• Define Machine Learning
• Discuss Machine Learning Use cases
• List the categories of Machine Learning
• Illustrate Supervised Learning Algorithms
• Identify and recognize machine learning algorithms around us
• Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.

• Python Revision (numpy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression
• Gradient descent

Hands-On: 

• Linear Regression – Using Boston Dataset

• Understand What is Supervised Learning?
• Illustrate Logistic Regression
• Define Classification
• Explain different Types of Classifiers such as Decision Tree and Random Forest

• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?

• Implementation of Logistic regression, Decision tree, Random forest

• Define the importance of Dimensions
• Explore PCA and its implementation
• Discuss LDA and its implementation

• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• Scaling dimensional model
• LDA

Hands-On:

• PCA
• Scaling

• Understand What is Naïve Bayes Classifier
• How Naïve Bayes Classifier works?
• Understand Support Vector Machine
• Illustrate How Support Vector Machine works?
• Hyperparameter optimization

• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter optimization
• Grid Search vs Random Search
• Implementation of Support Vector Machine for Classification

• Implementation of Naïve Bayes, SVM

•               o K - means Clustering
•               o C - means Clustering
•               o Hierarchical Clustering

• What is Clustering & its Use Cases?
• What is K-means Clustering?
• How K-means algorithm works?
• How to do optimal clustering
• What is C-means Clustering?
• What is Hierarchical Clustering?
• How Hierarchical Clustering works?

• Implementing K-means Clustering
• Implementing Hierarchical Clustering

• Define Association Rules
• Learn the backend of recommendation engines and develop your own using python

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering

• Apriori Algorithm
• Market Basket Analysis

• Explain the concept of Reinforcement Learning
• Generalize a problem using Reinforcement Learning
• Explain Markov’s Decision Process
• Demonstrate Q Learning

Topics: 

• What is Reinforcement Learning
• Why Reinforcement Learning
• Elements of Reinforcement Learning
• Exploration vs Exploitation dilemma
• Epsilon Greedy Algorithm
• Markov Decision Process (MDP)
• Q values and V values
• Q – Learning
• α values

• Calculating Reward
• Discounted Reward
• Calculating Optimal quantities
• Implementing Q Learning
• Setting up an Optimal Action

• Explain Time Series Analysis (TSA)
• Discuss the need of TSA
• Describe ARIMA modelling
• Forecast the time series model

• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

• Checking Stationarity
• Converting a non-stationary data to stationary
• Implementing Dickey Fuller Test
• Plot ACF and PACF
• Generating the ARIMA plot
• TSA Forecasting

• Discuss Model Selection
• Define Boosting
• Express the need of Boosting
• Explain the working of Boosting algorithm

• What is Model Selection?
• Need of Model Selection
• Cross – Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

• Cross Validation
• AdaBoost

• How to approach a project
• Hands-On project implementation
• What Industry expects
• Industry insights for the Machine Learning domain
• QA and Doubt Clearing Session