Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial
Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial
Machine learning is currently one of the hottest trends in the market,
with Gartner predicting that by 2022, 40% of new application development projects will require machine learning co-developers.
These projects are expected to generate around $3.9 trillion in revenue.
To meet this demand, Emma Global has designed a well-structured machine learning full course.
Before we dive into the course, let me introduce myself.
My name is Emmanuel from Emma Global, and I will be your guide through this entire machine learning journey.
Introduction to Machine Learning
This course is designed in a way that allows you to get the most out of it.
Starting with beginner level topics, we gradually move towards more advanced topics.
Today's agenda is divided into six different modules:
Module 1: Introduction to machine learning, including what it is,
how it differs from artificial intelligence, the planning, various types of applications,
and a basic demo in Python.
Module 2:Statistics and probability, covering descriptive statistics,
inferential statistics, Bayes' theorem, and more.
Module 3:Supervised learning,
which focuses on regression and classification problems with labeled data sets.
The algorithms covered include linear regression, logistic regression, Naive Bayes,
random forest, decision trees, and more.
Module 4:Unsupervised learning,
which deals with unlabeled data sets and includes algorithms such as k-means and Apriori.
Module 5: Reinforcement learning,
\ which covers reinforcement learning and the Q-learning algorithm.
Module 6: Industry readiness,
including three different projects based on supervised learning, unsupervised learning,
and reinforcement learning,
as well as a discussion of the skills needed to become a machine learning engineer and important interview questions.
Module 1: Introduction to Machine Learning
What is Machine Learning?
Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
The process of learning begins with observations or data,
such as examples, direct experience, or instruction,
in order to look for patterns in data and make better decisions in the future.
Difference Between AI and ML
Artificial Intelligence is a broader concept that aims to create intelligent machines that can simulate human thinking capability and behavior. Machine Learning, on the other hand,
is an application of AI that allows systems to learn and improve from experience automatically.
Types of Machine Learning
1. Supervised Learning The algorithm learns from labeled training data,
helping to predict outcomes for unforeseen data.
2. Unsupervised Learning
Deals with unlabeled data and finds hidden patterns or intrinsic structures in the input data.
3. Reinforcement Learning
A learning method that interacts with its environment by producing actions and discovering errors or rewards.
Applications of Machine Learning
Healthcare Disease identification, personalized medicine
Finance Fraud detection, risk assessment
Retail Recommendation systems, inventory planning
Transportation Self-driving cars, traffic prediction
Manufacturing Predictive maintenance, quality control
Basic Python Demo
Let's look at a simple example of implementing a machine learning model using
Python scikit-learn library to predict housing prices based on features like size,
number of bedrooms, and location.
Module 2: Statistics and Probability
Descriptive Statistics
Mean, Median, Mode Central tendency measures
Range, Variance, Standard Deviation Measures of dispersion
Percentiles and Quartiles Positional measures
Inferential Statistics
Hypothesis Testing Making inferences about populations
Confidence Intervals Range of values that likely contains the population parameter
P-values Probability of obtaining test results at least as extreme as the observed results
Bayes' Theorem
Understanding conditional probability and how prior beliefs can be updated with new evidence.
Probability Distributions
Normal Distribution Bell-shaped curve common in natural phenomena
Binomial Distribution Discrete probability distribution
Poisson Distribution Discrete probability distribution for countable events
Module 3: Supervised Learning
Linear Regression
A linear approach to modeling the relationship between a dependent variable and one or more independent variables.
Logistic Regression
Used for binary classification problems,
predicting the probability of an instance belonging to a particular class.
Decision Trees
A flowchart-like structure where each internal node represents a feature,
each branch represents a decision rule, and each leaf node represents an outcome.
Random Forest
An ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes or mean prediction of the individual trees.
Naive Bayes
A probabilistic classifier based on applying Bayes' theorem with strong independence assumptions between the features.
Support Vector Machines (SVM)
Effective in high-dimensional spaces and when there is a clear margin of separation between classes.
K-Nearest Neighbors (KNN)
A non-parametric method used for classification and regression,
where the output is based on the k closest training examples in the feature space.
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