Become a Confident Machine Learning and AI Programmer and grab the highly paid jobs of 2021 and future!

The Future is banking on Machine Learning and Artificial Intelligence.

Data roles are one of the highest-paid individuals in the tech industry. For the last four years, Data science has been featured as a top career by Glassdoor. Python plays a vital role in AI coding language by providing it with good frameworks like scikit-learn: Machine Learning in Python, which fulfils almost every need in this field and D3.js – Data-Driven Documents in JS, which is one of the most powerful and easy-to-use tools for visualization.

We cover a wide variety of topics, including:

1. Feature Engineering : Feature Selection, Handling missing values, Handling outliers, Binning, Encoding, Feature Scaling

2. Supervised Learning :

• Regression : Linear Regression, Regression Trees, Polynomial Regression, Regularization
• Classification : Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Naïve Bayes, KNN

3. Unsupervised Learning :

• Clustering : K-means clustering
• Association: PCA (Principal component analysis), LDA (Linear Discriminant analysis)

4. Neural Networks : ANN, CNN

5. Natural Language Processing (NLP)

Below are full details of Modules and Sub-Modules that we will cover in this course:

• Machine Learning Applications
• Life cycle of Machine Learning
• Install Anaconda & Python
• AI vs Machine Learning
• How to Get Datasets
• Data Preprocessing
• Supervised Machine Learning
• Unsupervised Machine Learning
• Supervised vs Unsupervised Learning
• Regression Analysis
• Linear Regression
• Simple Linear Regression
• Multiple Linear Regression
• Backward Elimination
• Polynomial Regression
• Classification Algorithm
• Logistic Regression
• Classification vs Regression
• Linear Regression vs Logistic Regression
• Association Rule Learning
• Confusion Matrix
• Cross-Validation
• Data Science vs Machine Learning
• Machine Learning vs Deep Learning
• Dimensionality Reduction Technique
• Overfitting & Underfitting
• What is P-Value
• Regularization in Machine Learning
• Support Vector Machine Algorithm
• Naïve Bayes Classifier
• Decision Tree Classification Algorithm
• Random Forest Algorithm
• Neural networks
• Artificial neural networks
• Convulational neural networks
• K-means clustering
• KNN (k-nearest neighbors)
• Hierarchal clustering
• Anomaly detection
• Principle Component Analysis
• Independent Component Analysis
• Apriori algorithm
• Singular value decomposition

Frequently Asked Questions

When does the course start and finish?

The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.

How long do I have access to the course?

How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.

What if I am unhappy with the course?

We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.