About Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations of data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
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Internship Tracks
Machine Learning
 1. Introduction to Machine Learning.
 2. How Machine Learning Useful in Daily Life
 3. Machine Learning Goals and Deliverables.
 4. Why Machine Learning
 5. Machine Learning Tools.
 1.Introduction to Python
 2.Anaconda Installation and Introduction to Jupyter Notebook
 1. Data Structures in Python (Lists, Tuples, Dictionaries, sets)
 1.Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
 1.Introuction to OOPS, Inheritence,Polymorphism,Encapsualtion,Abstraction
 1. Introduction to Numpy.
 2. Operations in Numpy
 1. Introduction to Pandas.
 2. Operations in Pandas  Pandas Basics, Indexing and selecting Data,Merge and Append, Grouping and Summarizing, Lambda functions and Pivot tables
 3. Introduction to Reading.
 1. Introduction to Matplotlib.
 2. Types of plots with Examples
 1. Introduction to Database design,.
 2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.
 3. Basics on Table creation, updating, modifying etc.
 4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.
 1. Business and Data Understanding
 2. CRISPDM Framework  Data Preparation, Modelling, Evaluation and Deployment
 1.Introduction to visualization and Importance of Visualization
 2. Introduction to various charts
 3. Data visualization toolkit in Python (Libraries or modules available in Python)
 4. Plotting Data in Python using matplotlib and seaborn  Univariate Distributions, Bivariate Distributions
 5. Plotting Time series data
 1. Introduction to Data Sourcing and various sources available for data collection
 2. Data Cleaning  Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data
 1. Data types  Numerical, Categorical (ordered and unordered)
 2. Univariate Analysis, Bivariate Analysis, Segmented univariate Analysis
 3. Derived Metrics and Feature Engineering
 1. Introduction to Outliers.
 2. Identify Outliers
 3. Outliers Handling using Imputation Techniques
 1. Introduction to inferential statistics  basics of probability, Random Variables, Expected value, Probability Distributions
 2. Discrete and Continuous Probability Distributions
 3. Central Limit Theorem  Introduction and Industrial applications
 1. understanding Hypothesis Testing, Null and Alternate Hypothesis, Industry Relevance
 2. Concepts of Hypothesis Testing  p value method, critical value method
 3. Types of Errors, T Distribution, other types of tests
 4. Industry Demonstration and A/B Testing
 1. Credit Analysis EDA
 2. GDP EDA Analysis
 1. Introduction to Machine Learning  Supervised and Unsupervised learning Methods
 1. Introduction to Regression and Best Fit Line
 2. Assumptions of Linear Regression (LINE)
 3. Cost Functions, Strength of Linear relationship  OLS, coefficient of correlation, coefficient of Determination
 4. Intuition to Gradient Descent for optimizing cost function
 5. Hypothesis Testing in Linear Regression
 6. Building a Linear Model  Reading Data, Cleaning Data, Libraries available  Sklearn, Statsmodel.api
 7. Model Building using Sklearn and Training and Test Data, Model Development, Model validation using Residual Analysis, Evaluation against the test Data
 1. Using Multiple Predictors for Linear Regression
 2. Introduction to overfitting, Multicollinearity
 3. Dealing with Categorical variables  OHE, Dummies, Label Encoding
 4. Building the model using statesmodel.api and importance of pvalues
 5. Model Evaluation Metrics  Coefficient of Determination, Adjusted R2, RMSE, AIC, BIC and other model evaluation Metrics
 6. Variable Selection  RFE, Step wise selection etc.
 7. Gradient Descent and Normal Equation for Multiple Linear Regression
 8. Industry Demonstration: Linear Regression Case Study
 1. Introduction to Classification
 2. Binary classification using univariate logistic regression
 3. Maximum Likelihood function, Sigmoid Curve and Best Fit
 4. Intuition of odds and logodds
 5. Feature selection using RFE
 6. Model evaluation  Confusion Matrix and Accuracy
 7. Why Accuracy is not Enough and introduction to sensitivity, specificity, precision, recall, area under curve
 8. Logistic Regression Case Study
 1. Understanding clustering with practical examples
 2. KMeans Clustering  understanding the algorithm
 3. Practical consideration for KMeans Clustering  Elbow curve, silhouette metric and hopkings test for clustering tendency of data, impact of outliers
Means Clustering:
 1. Hierarchical clustering Algorithm
 2. Interpreting the dendogram and Types of Linkages
 3. Comparison of Kmeans clustering and Hierarchical clustering  advantages and disadvantages
Hierarchical Clustering:
 1. Intuition behind PCA and practical examples
 2. Variance as information and basis transformation of vectors
 3. Singular Value Decomposition and Identifying optimum principal components using scree plots
 4. Model building with PCA
 5. Advantages of PCA and Limitations
 1. Introduction to SVM and How does it works.
 2. Advantages and Disadvantages of SVM
 3. Kernal Functions in used in SVM
 4. Applications of SVM
 5. Implementation of SVM using Python
SVM:
 1. Introduction to KNN and How does it works.
 2. Advantages and Disadvantages of KNN
 3. Applications of KNN
 4. Implementation of KNN using Python
KNN:
 1. Intoduction to Naive Bayes
 2. Advantage and Disadvantage of Naive Bayes
 3. Applications of Naive Bayes
 4. Implementation of Naive Bayes using Python
Naive Bayes:
 1. Introduction to decision trees and Interpretation
 2. Homogeneity measures for splitting a node 1. Gini Index 2. Entropy 3. R2
 3. Understanding Hyper parameters  Truncation and Pruning
 4. Advantages and Disadvantages
 1. Introduction to ensembling, bagging and intuition
 2. Random Forest  Introduction and Hyperparamters
 3. Building a model using Random Forest
 4. Hyperparameters impact on model and tuning them
 5. Importance of predictors using Random Forrest
Decision Trees:
Random Forest:
 1. Intuition behind Boosting
 2. Introduction to Boosting Algorithms : XGBoost, lightGBM, Catboost
 3. Advantages of Boosting Algorithms
 4.XGBoost Model Building and importance of various Hyper parameters
 5. Hyperparameter tuning for XGBoost
 Correlation and Regression Analysis of Physicochemical Parameters of River Water for the Evaluation of Percentage

Telecom Churn  Group Case Study
 1. Introduction to Time Series
 2. Trend and seasonality
 3. Decomposition
 4. moothing (moving average)
 5. SES, Holt & HoltWinter Model
 1. AutoRegression, Lag Series, ACF, PACF
 2. IADF, Random walk and Auto Arima
 1. Introduction to Text Mining
 2. Text cleaning, regular expressions, Stemming, Lemmatization
 3. Word cloud, Principal Component Analysis, Bigrams & Trigrams
 4. Text classification, Document vectors, Text classification using Doc2vec
 sentiment analysis Twiter Data