## Course outline for Data Science and Machine Learning

### Pre-requisites for learning Data Science and Machine Learning

- The participants must be comfortable with programming constructs in Python
- Basic algebraic concepts is a pre-requisite, knowledge of statistics is preferable

### Lab Setup

- Hardware Configuration
- All participants must have a laptop with Internet connectivity
- At least 4GB of RAM and 20GB of free hard-disk space

- Software Configuration
- Install Python and R before the start of the training

### Duration

- 5 Days

### Training Mode

#### Online training for Data Science and Machine Learning

We provide:

- Instructor led live training
- Self-paced learning with access to expert coaches
- 24x7 access to cloud labs with end to end working examples

All jnaapti sessions are 100% hands-on. All our instructors are engineers by heart. Activities are derived from real-life problems faced by our expert faculty. Self-paced hands-on sessions are delivered via Virtual Coach.

#### Classroom training for Data Science and Machine Learning

Classroom sessions are conducted in client locations in:

- Bengaluru
- Chennai
- Hyderabad
- Mumbai
- Delhi/Gurgaon/NCR

Note: Classroom training is for corporate clients only

### Detailed Course Outline for Data Science and Machine Learning

#### Some Pre-requisites

- Linear Algebra
- Probability
- Probability Distribution

#### Overview of Data Science and Machine Learning

- What is Data Science and Data Analysis?
- Cleaning up the data before analysis
- Data Visualization

- What is Machine Learning?
- Supervised v/s unsupervised learning
- Aritificial Intelligence
- Deep Learning

#### Some Basic Machine Learning Concepts

- Features, Labels and Classifiers
- Supervised Learning Algorithms
- Naive Bayes
- Decision Trees
- Support Vector Machines
- Kernel Trick
- Principal Component Analysis

- Unsupervised Learning
- Clustering algorithms
- K-means

- Neural Networks

#### Applications of Machine Learning

- Spam Detection
- Recommendation System
- Handwriting Recognition
- Face Recognition

#### Natural Language Processing

- Working with unstructured data
- Working with HTML data, scraping
- Token
- Introduction to BNF
- Regular Expression concepts

#### Technologies used in Machine Learning

- Octave
- Matlab
- Python
- R
- Julia

#### Python - Machine Learning Libraries

- numpy
- sklearn
- scipy
- textblob
- nltk
- textblob
- matplotlib
- pandas
- Jupyter
- Tensorflow
- Keras

#### Overview of sklearn

- Naive Bayes
- Decision Trees
- Support Vector Machines
- Kernel Trick
- Principal Component Analysis
- Clustering algorithms
- K-means
- Neural Networks
- Persisting the models using pickle

#### Overview of Pandas

- Data Structures - Series, Data Frames
- Importing, Analysing and Exporting data with Pandas
- Descriptive Stastics
- Aggregation APIs
- Transform APIs
- Iteration on data structures
- Working with text data

#### Introduction to nltk and textblob

- Tokenizer
- POS tagger
- Text classification
- Vectorizer
- tf-idf
- Sentiment Analysis with textblob - a case study

#### Introduction to R

- Installing R and R Studio
- Atomic Data Types
- Control Structures in R
- Functions in R
- Vectors and Lists
- Matrices
- Data Frames
- Operations on Vectors, Lists, Matrices and Data Frames
- Loading, analysing and storing your data
- Basic statistical functions in R
- Data Transforms in R using dplyr
- Machine Learning in R
- Plotting your analysis

#### Neural Networks

- Perceptrons
- Sigmoid Neurons
- Gradient Descent
- Back propagation
- Introduction to Deep Learning

#### Jupyter

- Installing Jupyter Notebook
- Running a Notebook server
- Notebook basics
- Using Jupyter with R (R Kernel)
- Securing your Jupyter notebook

#### Data Visualization

- Need for visualization
- Distribution of one variable - Histogram, Density plot
- Distribution of multiple variables - Heat map, Surface plots
- Distribution summary using Box plot, Violin plot
- Visualization libraries in Python
- matplotlib
- Seaborn
- ggplot and ggplot2
- Altair

- Visualization with R