Course Outline:
DURATION: 12 Days
Module 1: Introduction to Python:
- Overview of Python.
- The Companies using Python.
- Different Applications where Python is used Discuss Python Scripts on UNIX/Windows Values, Types, Variables.
- Operands and Expressions Conditional Statements Loops.
- Command Line Arguments.
- Writing to the screen.
Hands On/Demo:
- Creating “Hello World” code Variables.
- Demonstrating Conditional Statements.
Python Variables and Data Types:
- Strings and related operations Tuples and related operations Lists and related operations.
- Dictionaries and related operations.
- Sets and related operations.
- Tuple – properties, related operations, compared with a list – properties, related operations.
- Dictionary – properties, related operations.
- Set – properties, related operations.
- Python Operators.
- Demonstrating Loops.
- Hands-on Session.
Day 2
Functions:
- Function Parameters Global Variables.
- Variable Scope and Returning Values Lambda Functions.
- Hands-on Session.
Packages and Module – Modules, Import Options, Sys Path:
- Working Libraries in Python.
- Introduction to NumPy, Pandas and Matplotlib.
- Data Handling using Pandas.
- Hands-on Session.
Day 3
Object-Oriented Concepts Standard Libraries Modules Used in Python:
- The Import Statements Module Search Path Package Installation Ways, Errors and Exception Handling.
- Handling Multiple Exceptions.
- Hands On/Demo.
- “Functions – Syntax, Arguments, Keyword Arguments, Return Values Lambda – Features, Syntax, Options,.
- Compared with the Functions Sorting – Sequences, Dictionaries, Limitations of Sorting”.
- Errors and Exceptions – Types of Issues, Remediation.
- Hands-on Session.
Data Manipulation:
- Basic Functionalities of a data object Merging of Data objects Concatenation of data objects.
- Types of Joins on data objects Exploring a Dataset.
- Analysing a dataset.
- Hands On/Demo.
- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples().
- GroupBy operations Aggregation Concatenation Merging.
- Joining.
Day 4
Working with Databases:
- Connecting to Database.
- Reading data.
- Inserting Data.
- Hands-on Session.
NumPy – arrays Operations on arrays:
- Indexing slicing and iterating Reading and writing arrays on files.
- Pandas – data structures & index operations.
- Reading and Writing data from Excel/CSV formats into Pandas matplotlib library.
- Grids, axes, plots.
- Markers, colours, fonts and styling.
- Types of plots – bar graphs, pie charts, histograms.
Hands On/Demo:
- NumPy library- Creating NumPy array, operations performed on NumPy array Pandas library- Creating series and dataframes, Importing and exporting data.
- Matplotlib – Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot.
Skills:
- Probability Distributions in Python.
- Python for Data Visualization.
- Hands-on Session.
Day 5
Working with Databases
- Connecting to Database.
- Reading data.
- Inserting Data.
- Hands-on Session.
NumPy – arrays Operations on arrays:
- Indexing slicing and iterating Reading and writing arrays on files.
- Pandas – data structures & index operations.
- Reading and Writing data from Excel/CSV formats into Pandas matplotlib library.
- Grids, axes, plots.
- Markers, colours, fonts and styling.
- Types of plots – bar graphs, pie charts, histograms.
Hands On/Demo:
- NumPy library- Creating NumPy array, operations performed on NumPy array Pandas library- Creating series and dataframes, Importing and exporting data.
- Matplotlib – Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot.
Skills:
- Probability Distributions in Python.
- Python for Data Visualization.
- Hands-on Session.
Day 6
Python Revision and Numpy, Pandas, Scikit learn, Matplotlib:
- Hands-on Session.
- Q & A.
What is Machine Learning?:
- Machine Learning Use-Cases Machine Learning Process Flow Machine Learning Categories Linear regression
Gradient descent.
Hands On/Demo:
- Linear Regression – Boston Dataset.
Skills:
- Machine Learning concepts Machine Learning types.
- Linear Regression Implementation.
- Hands-on Session.
Day 7
Supervised Learning – I:
- Algorithm for Decision Tree Induction Creating a Perfect Decision Tree Confusion Matrix.
- What is Random Forest?.
Hands On/Demo:
- Implementation of Logistic regression Decision tree.
- Random forest.
Skills:
- Supervised Learning concepts.
- Implementing different types of Supervised Learning algorithms Evaluating model output.
- Un-Supervised Learning concepts.
- Dimensionality Reduction.
Introduction to Dimensionality Why Dimensionality Reduction PCA:
- Factor Analysis.
- Scaling dimensional model.
Hands-On/Demo:
- PCA.
- Scaling.
Skills:
- Implementing Dimensionality Reduction Technique.
- Hands-on Session.
Day 8
Supervised Learning – II:
- Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc..
- What is Naïve Bayes? How Naïve Bayes works?.
- Implementing Naïve Bayes Classifier What is Support Vector Machine?.
- Illustrate how Support Vector Machine works? Hyperparameter Optimization.
- Grid Search vs Random Search.
- Implementation of Support Vector Machine for Classification.
Hands-On/Demo:
- Implementation of Naïve Bayes, SVM.
- Hands-on Session.
Day 9
Implementing different types of Supervised Learning algorithms:
- Evaluating model output.
Unsupervised Learning:
- What is Clustering & its Use Cases? What is K-means Clustering?.
- How does K-means algorithm work? How to do optimal clustering.
- What is C-means Clustering? What is Hierarchical Clustering?.
- How Hierarchical Clustering works?.
- Hands-On/Demo.
- Implementing K-means Clustering.
- Implementing Hierarchical Clustering.
- Hands-on Session.
Day 10
Time Series Analysis:
- What is Time Series Analysis?.
- Importance of TSA.
- White Noise AR model MA model ARMA model.
- ARIMA model.
- Stationarity ACF & PACF.
Hands on/Demo:
- Checking Stationarity.
- Converting a non-stationary data to stationary Implementing Dickey-Fuller Test.
- Plot ACF and PACF.
- Generating the ARIMA plot TSA Forecasting.
- TSA in Python.
Model Selection and Boosting:
- What is Model Selection?.
- Cross-Validation What is Boosting?.
- How Boosting Algorithms work? Types of Boosting Algorithms.
- Adaptive Boosting.
Hands on/Demo:
- Cross-Validation.
- AdaBoost.
Skills:
- Model Selection.
- Boosting algorithm using python.
- Sequences and File Operations.
- Hands-on Session.
Day 11
Introduction to Text Mining and NLP:
- Overview of Text Mining Need of Text Mining.
- Natural Language Processing (NLP) in Text Mining Applications of Text Mining.
- OS Module.
- Reading, Writing to text and word files Setting the NLTK Environment Accessing the NLTK Corpora.
Hands On/Demo:
- Install NLTK Packages using NLTK Downloader.
- Accessing your operating system using the OS Module in Python Reading & Writing .txt Files from/to your Local.
- Reading & Writing .docx Files from/to your Local.
Working with the NLTK Corpora:
- Cleaning using NLTK.
- Tokenization Frequency Distribution.
- Different Types of Tokenizers Bigrams, Trigrams & Ngrams Stemming.
- Lemmatization Stopwords POS Tagging.
- Named Entity Recognition.
Hands On/Demo:
- Tokenization: Regex, Word, Blank line, Sentence Tokenizers Bigrams, Trigrams & Ngrams.
- Stopword Removal POS Tagging.
Named Entity Recognition (NER):
- Analyzing Sentence Structure.
- Syntax Trees Chunking Chinking.
- Context Free Grammars (CFG).
Hands On/Demo:
- Parsing Syntax Trees Chunking.
- Chinking.
- Automate Text Paraphrasing using CFG’s.
Day 12
Text Classification – I:
- Machine Learning: Brush Up Bag of Words.
- Count Vectorizer Term Frequency (TF).
- Inverse Document Frequency (IDF).
Hands On/Demo:
- Demonstrate Bag of Words Approach Working with CountVectorizer().
- Using TF & IDF.
- Hands-on Session.
Text Classification – II:
- Converting text to features and labels.
- Multinomial Naiive Bayes Classifier Leveraging Confusion Matrix.
Implement Machine Learning along with Text Processing:
- Hands-On.
- Sentiment Analysis.
- Hands-on Session.