Machine Learning with Python Hot & new
Transform data into intelligent systems with Python's Machine Learning magic.
By Von wick
Last update 10 Sep. 2019
This course includes
- 48.5 hours on-demand video
- 57 articles
- 12 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Description
Learn the fundamentals of Machine Learning using Python, including algorithms, data processing, and predictive modeling techniques. This course equips you with skills to develop AI-driven solutions.
Objective of the Course
- Hands-on projects to master real-world ML applications.
- Deep dive into Python libraries like Scikit-learn, Pandas, and Matplotlib.
- Gain practical insights into supervised and unsupervised learning.
Data Science with Artificial Intelligence 013 lectures
- Lecture1.1 Data science & its importance
- Lecture1.2 Key Elements of Data Science
- Lecture1.3 Data Warehousing
- Lecture1.4 Business Intelligence
- Lecture1.5 Data Visualization
- Lecture1.6 Data Mining
- Lecture1.7 Machine Learning
- Lecture1.8 Artificial Intelligence
- Lecture1.9 Cloud Computing
- Lecture1.10 Big Data
- Lecture1.11 Artificial Intelligence: A previewPreview
- Lecture1.12 What is Artificial Intelligence & its importance
- Lecture1.13 Artificial Intelligence vs Machine Learning
Introduction to Machine Learning 07 lectures
- Lecture2.1 What is Machine Learning (ML)?
- Lecture2.2 How machines learnPreview
- Lecture2.3 Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement
- Lecture2.4 Basics of Classification, Regression and Clustering algorithms
- Lecture2.5 Creating your first Prediction Model
- Lecture2.6 Training & Model evaluation
- Lecture2.7 Choosing Machine Learning algorithm
Basics (Maths with Statistics) 04 lectures
Python for Data Science 05 lectures
- Lecture4.1 A quick crash course on basics of Python
- Lecture4.2 Python Object and Data Structure
- Lecture4.3 Python Comparison Operators
- Lecture4.4 Python Statements
- Lecture4.5 Methods and Functions
- Lecture4.6 Milestone Project – 1
- Lecture4.7 Object-Oriented Programming
- Lecture4.8 Modules and Packages
- Lecture4.9 Errors and Exception Handling
- Lecture4.10 Milestone Project – 2
- Lecture4.11 Built-in Functions
- Lecture4.12 Python Decorators
- Lecture4.13 Python Generators
- Lecture4.14 Final Capstone Python Project
- Lecture4.15 Advanced Python Modules
- Lecture4.16 Advanced Python Objects and Data Structures
- Lecture4.17 Advanced OOP
- Lecture4.18 Parallel Processing
- Lecture4.19 Python Packages [Statsmodels, Sci-kit Learn, Scipy, Numpy, Pandas, Matplotlib, Seaborn, etc.]
- Lecture4.2 Python Object and Data Structure
Data Processing for Machine Learning 010 lectures
- Lecture5.1 Data Collection & Preparation
- Lecture5.2 Data Mugging
- Lecture5.3 Outlier Analysis
- Lecture5.4 Missing value treatment
- Lecture5.5 Feature Engineering
- Lecture5.6 Data Transformation
- Lecture5.7 Normalization vs Standardization
- Lecture5.8 Creating Dummies
- Lecture5.9 Dimensionality Reduction
- Lecture5.10 Principal Component Analysis
Advanced Machine Learning Algorithms 017 lectures
- Lecture6.1 Supervised Machine Learning algorithms
- Lecture6.2 Linear RegressionPreview
- Lecture6.3 Logistic RegressionPreview
- Lecture6.4 Decision/Classification
- Lecture6.5 Tree Ensemble Models
- Lecture6.6 Bagging
- Lecture6.7 Boosting
- Lecture6.8 Random Forest
- Lecture6.9 K-Nearest Neighbours (KNN)
- Lecture6.10 Naive Bayes
- Lecture6.11 Neural Network (Deep Learning)
- Lecture6.12 Support Vector Machine
- Lecture6.13 Unsupervised Machine Learning algorithms
- Lecture6.14 Clustering with K-means Clustering
- Lecture6.15 Bias-Variance Trade off
- Lecture6.16 Regularization
- Lecture6.17 Parameter tuning & grid search optimization
Case Study and Project 017 lectures
Job Roles & Placement Details
- Job Roles: Machine Learning Engineer, AI Specialist, Data Scientist
- High placement rates in AI-driven industries, startups, and tech firms.
- Collaboration with hiring partners to connect with top recruiters.
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