• Intro to Numpy Arrays
• Creating ndarrays
• Indexing
• Data Processing using Arrays
• File Input and Output
• Getting Started with Pandas

• Data Acquisition(Import & Export)
• Indexing
• Selection and Filtering
• Sorting & Summarizing
• Descriptive Statistics
• Descriptive Statistics
• Combining and Merging Data Frames
• Removing Duplicates
• Discretization and Binning
• String Manipulation

• Line Plots
• Bar Charts
• Pie Charts
• Histograms
• Scatter Plots
• Parallel Coordinates

• Intro to Machine Learning
• Intro to Sklearn Library and Statsmodels

• Simple Regression Analysis
• Multiple Regression

• Lazy Learning Notion
• Computation of Distance Matrix
• The Optimum K value
• Data Transformations as a Pre Processing Phase
• Model Building on Training Data Set
• Model Validation on Testing Data Set
• Evaluation of Model
• Advantages & Disadvantages of KNN Models

• Bayesian Theorem
• Probabilities – The Prior and Posterior Probabilities
• Conditional and Joint Probabilities Notion
• Traditional Approach – Extract Important Features
• Naive Approach – Independence of Features Assumption>
• Data Processing – Discretization of Features
• Model Building / Testing / Validation
• Advantages & Disadvantages of Naïve Bayes Models

• Understanding SVM
• Concepts of Linearly seperable vs non seperable data
• Build the Model
• Training the Model
• Testing and Validation
• Tuning the Model

• Decision Trees, Bagging
• Random Forests, Boosted Trees

• Business Analysts with well-versed knowledge of data analyzing
• Programmers, handling programming techniques
• Data Analyst with skills on data extraction and data wrangling
• Managers interested in the latest business analytics paradigm
• Data analyst professionals employing data to make a business decision

Machine Learning with Python Training does not require any mandatory skills. However, following prerequisites will helpful to understand bit quicker.

• Knowledge on python
• Data Analysis Practice with any tool

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