About Machine Learning with Python

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

Course Content

Statistics and its application in Analytics

  • Data Analysis and use of Statistical Techniques using Excel
  • Hypothesis Testing and its applications
  • Various tests like z-test, t-test and f-test, ANOVA, Chi Square Test
  • Measures of Central Tendency and Different kind of Distributions
  • Univariate and Bi-variant Analysis
  • Statistical Background of the below mentioned ML techniques

Python basics and Predictive Modelling

  • Introduction to Python and Python Library
  • Base and other Advanced Packages
  • Data Importing and Exporting (Importing from various sources like Excel, SQL)
  • Data Manipulation- data structure, Control Structures (conditional Statements, Loops, apply functions), Sorting Data
  • Merging and Appending Data, summarizing Data, Reshaping Data,
  • Sub setting Data
  • Data Type Conversions, Sampling, Renaming-formatting data, Handling duplicates/Missing values
  • Data Visualization- Histogram, Dot Plots, Bar Plots, Line Charts, Pie Charts, Boxplots, Scatterplots Variable Reduction Technique like Factor Analysis- PCA
  • Machine Learning – Linear Regression, Logistic Regression, Clustering , Decision Tree , Random Forrest, Naïve- Bayes, Concept of over fitting and under fitting , Cross Validation
  • Ensemble Models
  • Practical Examples of all the above listed ML techniques with result interpretation on Testing Dataset
  • Practical Case studies to cover all the ML techniques

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