Machine Learning Course Details

Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems.  Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters.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 learn for themselves.  Machine Learning (ML), globally recognized as a key driver of digital transformation, will be responsible for cumulative investments of $58 billion by the end of 2021.

Course Curriculum

Basic Probability and Terms

Events and their Probabilities | Rules of Probability | Conditional Probability and Independence | Permutations and Combinations | Bayers Theorem | Descriptive Statistics | Compound Probability | Conditional Probability

Probability Distributions

Types of Distributions | Functions of Random Variables | Probability Distribution Graphs | Confidence Intervals

Data Transformation and Quality Analysis

Merge, Rollup, Transpose and Append | Missing Analysis and Treatment Outlier Analysis and Treatment

Exploratory Data Analysis

Summarizing and Visualizing the Important Characteristics of Data | Hypothesis Testing | Visualizations | Univariates, Bivariates | Crosstabs, Correlation

Introduction To Python

Python Basics | Spyder IDE | Jupyter Notebook | Floats and Strings Simple Input & Output | Variables | Single and Multiline Comments

Control Structures

Booleans and Comparisons | Conditional Statements (IF ELSE) | Operator Precedence | Lists – Operations and Functions

Functions and Modules

Function Arguments | Comments and Doc Strings | Functions as Objects Modules | Standard Lib and Pip

Exceptions and Files

Exception Handling | Raising Exceptions | Assertions | Working With File

Linear Regression

Implementing Simple & Multiple Linear Regression with Python | Making Sense of Result Parameters | Model Validation | Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity | Prediction and Confidence Intervals | Use Cases

Logistics Regression

Implementing Logistic Regression with Python | Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures | Model Validation: Cross Validation, ROC Curve, Confusion Matrix | Use Cases

Decision Trees

Implementing Decision Trees using Python | Homogeneity | Entropy Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Errors


Introduction to Pandas | IO Tools | Basics of NumPy | NumPy Functions Pandas – Series and Data frames,

Scikit Learn

Introduction to SciKit Learn | Load Data into Scikit Learn | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classification & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Decide What’s the Best Model for Every Scenario.

3 Projects

Linear + Logistics + Decision trees

Introduction To Machine Learning

What is Machine Learning? | End-to-end Process of Investigating Data Through a Machine Learning Lens | Evolution and Trends | Application of Machine Learning | Best Practices of Machine Learning

Machine Learning Algorithims

Classification | Regression | Collaborative Filtering | Clustering Principal Component Analysis

Neural Networks

Understanding Neural Networks | The Biological Inspiration | Perceptron Learning & Binary Classification | Backpropagation Learning | Learning Feature Vectors for Words | Object Recognition


Keras for Classification and Regression in Typical Data Science Problems | Setting up KERAS | Different Layers in KERAS | Creating a Neural Network Training Models and Monitoring | Artificial Neural Network


Introducing Tensorflow | Neural Networks using Tensorflow | Debugging and Monitoring | Convolutional Neural Networks | Unsupervised Learning

2 Projects