Let’s transform data into information and information into insight
Machine learning is one of the most popular data science methodologies. Machine learning differs from other computer-assisted decision-making processes in that it uses data to create prediction algorithms. Some of the most popular machine learning products you will come across in this course include building Chatbot, speech recognition, movie recommendation systems, and spam detectors. This course will teach you popular machine learning algorithms, starting from the basics of python programming to data visualization, Understanding machine learning with Deployment of ML models, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a data set to discover potentially predictive relationships.
What you'll learn
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
Requirements
- Desktop/ Laptop
- Internet connectivity min 2 Mbps
Target Audience
- Data scientist
- Software Developer
- Machine learning
- Data analytics
Introduction with AI & Machine Learning
Data Science vs Data Engineering vs Data Analysis vs AI
Use of Data in the world of AI
Basic Linux/Windows Commands
Why Python?
Installing & Setting up Python on System
Understanding its Command Line & Scripts
Simple Python Program
Python Revisit: Keywords, Data Types, Operators
Conditional/Looping/Error Handling in Python
Comprehensions
Python User Defined Functions
Python Generators
Lambda Expressions
Python Modules: Usage and Installation
Understanding the OOP of Python
Types of DATA?
The key steps of Data Analysis
The file handling in python
Dealing with Excel/Json/CSV/txt files
What are SQL based Database
Basic SQL Operations
Python Numpy Arrays
Creating, Accessing, Manipulating Numpy Array
Numpy Data Types
Array Attributes
Data Operations
Arithmetic and Statistical Methods
Sort, Search, Count
File Handling with Numpy
Exercise – 1
The Series and DataFrame
Creating, Accessing, Manipulating Pandas Data
Series and DataFrame Attributes & Basic Functions
Iteration on Data
Statistical Functions; String Functions
Logical Indexing; Sorting & Reindexing
Merging, Joining & Concatenation of Data
Exercise – 2
Understanding Kaggle/Hackerearth Platforms
Pandas File Handling
Grouping Data
Function Application
Missing Data & Treatment
Date & Time Functionality
Exercise-3
Project 1: “Movie Recommendation System”
How Data is Beautiful?
Visualization Libraries in Python
MATPLOTLIB PYPLOT: line, scatter, pie, box, area etc
Decorating the plots using Matplotlib
(labels, colors, markers, legend, grids, figure sizes etc)
The Subplots and axes in matplotlib
Showing Images
Exercise – 4
Pandas Visualization: Basic Plots
Bar, barh, hist, box, kde, density, area, scatter, hexbin, pie plots
Plotting with Missing Data
Exercise – 5
Project 2: “Descriptive Analytic Model of Restaurant Billing & Tips Data”
Understanding the concept of Machine Learning
The Flow of Machine Learning
The Mathematics Required for ML
Types of Learning and their sub-categories
The Scikit-learn Library
REGRESSION: Linear Regression
The Line Equation; Fitting Data in Model
Performance Evaluation of Model
Project 3: “Real-estate House Price Predictor Model”
Exercise – 6
Multiple Linear Regression: Case-study
Polynomial Regression: The Non-linearity in Data
Project 4: “Startup Profit Prediction System”
Project 5: “Companies Valuation Prediction”
Exercise – 7
Logistic Regression: Concept
Dealing with missing data in real-time information
Exercise – 8
Project 6: “Flower Species Classification Model”
The Information Theory
Decision Trees Classifier
Random Forest Classifier
Project 7: “Heart Disease Detection App”
What is NLP?
Linguistic to Natural Language
Text and Speech Processing
Text to Speech and Speech to Text Modules in Python
Morphological Analysis
Syntactic Analysis
Word Clouds
Libraries in Python
Exercise – 9
Project 8: “Fake News Detection System”
The Probability
Bayes Theorem
Naïve Bayes Algorithm for Machine Learning
Naive Bayes Algo implementation
Project 9: “Email SPAM Detection Application”
Introduction with Flask
Deployment of Machine Learning Models over GITHUB
and Cloud Services (AWS/ Google/ and similar platforms)
QUERY SESSION
Easy and advanced Data Visualization from Seaborn
Categorical, Distributive, Regression, Matrix, Grid Plots
Customizing Color Themes
Some Datastore
Exercise – 10
The Real-time challenges of Data Visualization
The Front End of ML – PLOTLY
Plotly Express for Quick and Fiery Interactive Graphs
Scatter, Pie, Line, Bubble, Bar, Error, Box, Histograms
Heatmaps, Funnel, Waterfall, Candlestick, Distribution Plots
Map Plotting
Exercise – 11
Graph Transformation, Filtering, Aggregation, Grouping
User Instructiveness with User Controller
Creating Dashboards (Introduction)
Project 10: “Weather Prediction System”
Kernel Nearest Neighbors (KNN)
Working with sound data
MFCC for speech processing
Project 11: “Music Genre Prediction System”
Support Vector Machines (SVMs)
The Hyperplane Concept
Trade-off between biases-variances
Project 12: “Finding the organic users on social media”
Exercise – 12
Clustering Theory
K-Means Clustering
Principal Component Analysis
Project 13: “Detecting Real versus Fraud Credit Card Applications”
What is Deep Learning?
Understanding Neural Network
Multi-layer perceptron Problem
Libraries for Deep Neural Network – sklearn, pytorch, TensorFlow, Keras
The MLP Classifier
Project 14: “Speech Emotion Recognition”
What is ANN?
The basic terminology – Layers, weights, biases, activation
functions, losses, optimizers, learning rate
The Concept of Forward Propagation
Backward Propagation
Gradient Descent & SGD
ANN Using TensorFlow
TensorFlow 1.x vs TensorFlow 2.x
Project 15: “MNIST Image Classification Model”
