Humans learn from experience and machines learn from data. Machine learning is the science of teaching computers to perform certain tasks. The algorithm learn with data without being explicitly programmed. We use it almost everywhere without actually knowing that machine learning is behind it. In this course, you will learn about some of the widely used algorithms and the methodologies for implementing them.
Why should you update your resume with Machine Learning Skills?
“2.5 quintillion bytes of data are created every day and it is expected to grow at a staggering pace in the days to come!” Machine learning was always just around the corner since generations, however, from past few decades, with the steep growth in technology, it has been the topmost priority. That is why, according to Gartner’s survey of Indian CIO’s, leading companies are willing to commit more than a third of their budget towards digital transformations involving data analytics and cloud infrastructures. India’s IT sector is all set to grow by at least 50% over the next couple of years, with at least 180,000 more jobs expectancy in the current year alone.
By the end of this course you will learn:
• Python for Data Science (python, Numpy Pandas, Scikit Learn and Matplotlib). • Fundamentals of Machine Learning • Building and training Machine Learning Algorithm
Its a blended course of 1 month , the classroom sessions will be for 24 - 30 Hours and the work assigned to you will also be of 30 Hours . So you implement practically what you learn . The assignments have to be implemented on our platform which will monitored and assessed by your Mentor.
Table of Content:
Module 1 Unit I: Machine Learning Introduction • Introduction to ML problems • ML terminologies • ML project workflow • ML real life examples
Unit II: Jupyter Notebook introduction • Working with Jupyter notebooks • Markdown and Code blocks • Keyboard shortcuts
Unit III: Python Basics • Python syntax • Basic data types • Basic data structures
Unit IV: Python advanced • Numpy Arrays • Plotting using Matplotlib • Pandas Dataframes • Introduction to Scikit Learn package
Module 2 Unit I: Regression Modeling • Introduction • Modeling concept • Example problem - Housing price
Unit II: Simple Linear Regression • Error metric - SSE, MSE, R Squared • Least Square algorithm • Gradient Descent Algorithm • Implementation using scikit-learn
Unit III: Multiple Linear Regression • Dummy variables • Error metric - SSE, MSE, R Squared • Gradient Descent Algorithm • Feature Selection (Incremental) • Implementation using scikit-learn
Unit IV: Polynomial Regression • Non-linear relationship • Higher order terms • Feature selection • Modeling concepts - Avoid overfitting • Implementation using scikit-learn
Module 3 Unit I: Classification Modeling • Introduction to Classification Models • Error Metrics : Accuracy Score • Confusion Matrix • Type1 and Type 2 errors • Decision boundaries
Unit II: Logistic Regression • Discrete outcomes • Logit function • Probability scores • Implementation using scikit-learn
Unit III: Support Vector Machines • Support Vectors • Decision boundary • Kernel trick • Hyperparameters and Model tuning • Implementation using scikit-learn
Unit IV: Decision Trees • Entropy • Using Entropy in classification • Information Gain • Tree pruning • Implementation using scikit-learn
Unit V: Random Forests • Bias variance errors • Ensembling • Randomness in Random Forest • Hyperparameters • Implementation using scikit-learn
Module 4 Unit I: Cluster Modeling • Introduction to clustering • Distance measures • Error metrics • Analysing cluster outputs
Unit II: Hierarchical Clustering • Agglomerative method • Divisive method • Understanding Dendrogram • Cutting the dendrogram for obtaining clusters • Implementation using scikit-learn
Unit III: K-Means Clustering • Distance measures • Centroids and their importance • Steps involved in K-Means • Local optima problem • Implementation using scikit-learn
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