Machine Learning with Python

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Machine Learning with Python

Enhance your skills in Machine Learning with our comprehensive course on Python.

Category Artificial Intelligence

Machine Learning with Python

What you'll learn

Fundamentals of Python and Machine Learning
Real-World Applications & Case Studies
Data Preprocessing & Feature Engineering
Handling Imbalanced Data & Anomaly Detection
Model Building & Evaluation
Hyperparameter Tuning & Optimization

Requirements

Good Wifi
Laptop

Description

Machine Learning with Python

This course provides a comprehensive introduction to Machine Learning (ML) using Python, covering key concepts, real-world applications, data preprocessing, model building, and optimization techniques. It combines statistics, machine learning, data analysis, and domain expertise to uncover patterns, trends, and correlations that can help organizations make informed decisions and drive business growth. Through hands-on projects and practical implementation, you will gain expertise in building and evaluating ML models using Python libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and more.

Gain a comprehensive introduction to Machine Learning (ML) using Python.
Learn key concep.ts, real-world applications, data preprocessing, model building, and optimization techniques
Combine statistics, machine learning, data analysis, and domain expertise to uncover patterns, trends, and correlations.
Develop skills in building and evaluating ML models using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and more.
Engage in hands-on projects and practical implementation to enhance expertise.

Who this course is for:

Beginners looking to start a career in Machine Learning & Data Science.
Software developers and engineers who want to apply ML techniques to real-world applications.
Anyone interested in building ML models using Python and understanding how AI is shaping industries.

Meet your instructors

Sourav Kapil

View Profile

Sourav Kapil is a Data Science and AI Trainer with over 4 years of professional experience in Machine Learning, Artificial Intelligence, and Data Science. Having trained 1,500+ students, he is passionate about making AI accessible and impactful for learners at all levels. With a strong background...

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Why enroll in this course?

Lead digital transformation in your organization by mastering the core concepts of generative artificial intelligence and its potential impact.

Master and integrate prompt engineering to optimize day-to-day tasks and automate workflows.

Foster an “AI friendly” culture in your organization by understanding the ethical aspects and the risks associated with the implementation of this technology.

Explore tools such as ChatGPT, as well as other emerging technologies, to improve productivity.

Interact with MIT experts, instructors, and peers in live synchronous sessions for a more comprehensive learning experience.

Access to rich supplementary resources provides additional materials and content for a more thorough educational journey.

Course Syllabus

What is AI & ML?
Applications of AI & ML
Machine Learning Workflow
Why Python for AI & ML?
Setting up Python Environment
Writing & Running Python Code

Installing & Importing Packages
Python Fundamentals
Variables & Identifiers

Assigning Values to Variables
Dynamic Typing in Python
Reserved Keywords in Python
Writing Meaningful Comments
Control Flow & Loops
Arithmetic, Comparison, Logical, Assignment, Membership Operators

Introduction to NumPy

Conditional Statements- if, elif, else Statements
Iterative Statements (Loops) - for and while Loops
Nested Loops
Loop Control Statements
break, continue, pass
Data Structures in Python
Lists & Tuples
Creating, Accessing, and Modifying Lists & Tuples
List & Tuple Methods
Dictionaries & Sets
Key-Value Pairs in Dictionaries
Set Operations
Advanced Control Statements
break, continue, pass (Detailed with Use Cases)
Functions in Python
User-defined Functions
Function Definition & Calling
Function Arguments & Return Values
Built-in Functions
Commonly Used Functions (len(), sum(), max(), etc.)
Lambda Functions
Anonymous Functions & Their Applications
Advanced Python Concepts
List & Dictionary Comprehensions
Writing Efficient Python Code
Examples & Use Cases
Exception Handling
try, except, finally Blocks
Handling Multiple Exceptions
Raising Custom Exceptions
NumPy Array vs Python List
Creation of 1D, 2D, and 3D Arrays
Special NumPy Functions: zeros(), ones(), full(), etc.
Random Number Generation
Data Type Conversion
Memory Management in NumPy
Arithmetic Operations
Statistical Operations
Sorting, Joining, and Splitting
Transpose, Reshape, and Broadcasting
Introduction to Pandas
Series and DataFrames
Creating DataFrames using Lists and Dictionaries
Insert and Delete Operations
Arithmetic Operations
Indexing and Slicing
Reading Data from CSV & JSON Files
Exploratory Data Analysis (EDA)
Handling Missing Data
Handling Duplicate Data
Outliers Detection and Treatment
Join, Concat, and Merge Operations
Date-Time Functionalities
groupby(), Transpose, Reshape

Introduction to Matplotlib
Line Plot
Bar Plot
Scatter Plot
Histogram
Pie Chart
3D Plots
Introduction to Seaborn
Boxplot
Distplot
Heatmap

Fundamentals of Statistics
Introduction to Statistics
Types of Statistics
Descriptive Statistics vs Inferential Statistics
Population and Sample Data
Sampling Techniques:
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Variables & Data Distributions
Types of Variables
Quantitative vs Qualitative Variables
Frequency and Cumulative Frequency
Measures of Frequency
Measures of Central Tendency
Measures of Dispersion and Variance
Z-Score and Standard Deviation
Measures of Position or Data Distribution
Quartile, Quantile, Percentile, Pentile, Decile
Five Number Summary
Interquartile Range (IQR)
Effect of Outliers and Its Removal
Outlier Detection using Boxplot
Probability Distributions & Correlation
Normal (Gaussian) Distribution
Properties of Normal Distribution
Central Limit Theorem
Covariance & Pearson Correlation Coefficient
Inferential Statistical Tests
Confidence Interval
Regression Analysis
Hypothesis Testing
T-Test, Z-Test, F-Test, ANOVA Test, Chi-Square Test
Null Hypothesis & Alternate Hypothesis
P-Value & Significance Level

Introduction to Machine Learning
Difference Between AI, ML and DL
Applications of Machine Learning
Types of Machine Learning
Supervised / Unsupervised / Reinforcement
Parametric vs Non Parametric
Introduction to Scikit-Learn
Sample Dataset in Scikit-Learn
Goodness of Fit
Overfitting and Underfitting
Bias-Variance Tradeoff
L1 and L2 Regularization
Imbalanced Data

Optimizers for ML
Gradient Descent
Evaluation Metrics
Regression Metrics
Classification Metrics

Null Values Imputation
Outlier Detection
Univariate/ Bivariate/ Multivariate Analysis
Encoding Technique
OHE, Label Encoding, Target Encoding
Curriculum

Normalization
Standardization

Filter Methods
Wrapper Methods
Embedding Methods

Feature Engineering
Model Selection
Holdout Validation
K-fold Cross Validation
Stratified K-fold
Hyperparameter Tuning
GridSearchCV
RandomizedSearchCV

Regression Algorithm
Linear Regression
Assumption of Linear Regression
Limitations of Linear Regression
Practical Implementation

Assumption of Polynomial Regression
Limitations of Polynomial Regression
Practical Implementation

Lasso Regression
Practical Implementation

Regression Algorithm

Assumption of Logistic Regression
Limitations of Logistic Regression
Practical Implementation

Entropy and Gini Index
Information Gain
Practical Implementation

The Advanced House Price Prediction project leverages state-of-the-artregression algorithms to provide accurate and reliable predictions for realestate prices. In the dynamic and competitive real estate market, having arobust prediction model is crucial for both buyers and sellers to makeinformed decisions. This project employs advanced machine learningtechniques to enhance the accuracy and efficiency of house pricepredictions.

The Netflix Movie Recommendation System project aims to enhance userexperience by leveraging advancedmachine learning algorithms to providepersonalised and relevant movierecommendations. With an extensivelibraryof movies and TV shows, Netflix recognizes the importance ofoffering tailored suggestions to users, ensuring they discover content thataligns with their preferences. This project utilizes content-basedrecommendation techniques to create a recommendation engine.

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