MachineLearningSyllabus.txt

harsha.delighted@gmail.com

Prep-program:

Python for Data Analysis: Get acquainted with Data Structures, Object Oriented Programming, Data Manipulation and Data Visualization in Python
Introduction to SQL: Learn SQL for querying information from databases
Math for Data Analysis: Brush up your knowledge of Linear Algebra, Matrices, Eigen Vectors and their application for Data Analysis

Statistical Essentials:
Inferential Statistics: Learn Probability Distribution Functions, Random Variables, Sampling Methods, Central Limit Theorem and more to draw inferences
Hypothesis Testing: Understand how to formulate and test hypotheses to solve business problems
Exploratory Data Analysis: Learn how to summarize data sets and derive initial insights

Machine learning:
Linear Regression: Learn to implement linear regression and predict continuous data values
Supervised Learning: Understand and implement algorithms like Naive Bayes and Logistic Regression
Unsupervised Learning: Learn how to create segments based on similarities using K-Means and Hierarchical clustering
Support Vector Machines: Learn how to classify data points using support vectors
Decision Trees: Tree-based model that is simple and easy to use. Learn the fundamentals on how to implement them

natural Language Processing:
Basics of text processing: Get started with the Natural language toolkit, learn the basics of text processing in python
Lexical processing: Learn how to extract features from unstructured text and build machine learning models on text data
Syntax and Semantics: Conduct sentiment analysis, learn to parse English sentences and extract meaning from them
Other problems in text analytics: Explore the applications of text analytics in new areas and various business domains

Neural networks and deep learning:
Information flow in a neural network: Understand the components and structure of artificial neural networks
Training a neural network: Learn the cutting-edge techniques used to train highly complex neural networks
Convolutional Neural Networks: Use CNN's to solve complex image classification problems
Recurrent Neural Networks: Study LSTMs and RNN's applications in text analytic
Creating and deploying networks using Tensorflow and keras: Build and deploy your own deep neural networks on a website, learn to use the Tensorflow API and Keras

Graphical Models:
Directed and Undirected Models: Learn the basics of directed and undirected graphs
Inference: Learn how graphical models are used to draw inferences using datasets
Learning: Learn how to estimate parameters and structure of graphical models

Reinforcement learning:
Introduction to RL: Understand the basics of RL and its applications in AI
Markov Decision Processes: Model processes as Markov chains, learn algorithms for solving optimisation problems
Q-learning: Write Q-learning algorithms to solve complex RL problems


Lesson 1: Download and Install Python and SciPy ecosystem.
Lesson 2: Get Around In Python, NumPy, Matplotlib and Pandas.
Lesson 3: Load Data From CSV.
Lesson 4: Understand Data with Descriptive Statistics.
Lesson 5: Understand Data with Visualization.
Lesson 6: Prepare For Modeling by Pre-Processing Data.
Lesson 7: Algorithm Evaluation With Resampling Methods.
Lesson 8: Algorithm Evaluation Metrics.
Lesson 9: Spot-Check Algorithms.
Lesson 10: Model Comparison and Selection.
Lesson 11: Improve Accuracy with Algorithm Tuning.
Lesson 12: Improve Accuracy with Ensemble Predictions.
Lesson 13: Finalize And Save Your Model.
Lesson 14: Hello World End-to-End Project.


https://in.pycon.org/cfp/2017/proposals/inferential-statistics-with-python~bq6yd/
https://www.analyticsvidhya.com/blog/2017/01/comprehensive-practical-guide-inferential-statistics-data-science/
https://github.com/rounakbanik/inferential_stats_pycon/blob/master/02-Sampling.ipynb
https://github.com/rounakbanik/inferential_stats_pycon

5supervised learning techniques- Linear Regression, Logistic Regression, CART, Naïve Bayes, KNN.
3unsupervised learning techniques- Apriori, K-means, PCA.
2ensembling techniques- Bagging with Random Forests, Boosting with XGBoost.

http://www.ritchieng.com/machine-learning-evaluate-linear-regression-model/

www.statisticshowto.com/inferential-statistics/
onlinestatbook.com/2/introduction/inferential.html
onlinestatbook.com/2/introduction/inferential.html
https://www.khanacademy.org/math/statistics-probability


https://www.kaggle.com/niteshsinghal/sms-spam-detection
https://www.kaggle.com/ismayc/predictive-analysis/notebook