## 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

#### Pandas

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

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

#### Tensorflow

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

#### 2 Projects

ANN + CNN