What you'll learn:
- Code for image recognition, handwriting recognition, data analysis, and create recurrent neural networks.
- using normal python libraries for data science
- using python libraries common for ai programming
- learn to use python for statistics
To be an AI and Data Science developer, you will have to know all of this. This is foundational stuff, even in 2025
AI and Data Science are taking over the world! Well sort of, and not exactly yet. This is the perfect time to hone you skills in AI,data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as the dominantlanguage in intelligence and machine learningprogramming because of its simplicity and flexibility, in addition to its great support for open source libraries and TensorFlow.
This video course is built for those with a NO understanding of artificial intelligence or Calculus and linear Algebra. We will introduce youto advanced artificial intelligence projects and techniques that are valuable for engineering, biological research, chemical research, financial, business, social, analytic, marketing (KPI), and so many more industries. Knowing how to analyze data will optimize your time and your money. There is no field where having an understanding of AI will be a disadvantage. AI really is the future.
We have many projects, such natural language processing , handwriting recognition, interpolation, compression, bayesian analysis, hyperplanes (and other linear algebra concepts). ALLTHECODEISINCLUDED ANDEASYTOEXECUTE. You can type along or just execute code in Jupyter if you are pressed for time and would like to have the satisfaction of having the course hold your hand.
Iuse the AI I created in this course to trade stock. You can use AI to do whatever you want. These are the projects which we cover.
For Data Science / Machine Learning / Artificial Intelligence
1. Machine Learning
2. Training Algorithm
3. SciKit
4. Data Preprocessing
5. Dimesionality Reduction
6. Hyperparemeter Optimization
7. Ensemble Learning
8. Sentiment Analysis
9. Regression Analysis
10.Cluster Analysis
11. Artificial Neural Networks
12. TensorFlow
13. TensorFlow Workshop
14. Convolutional Neural Networks
15. Recurrent Neural Networks
Traditional statistics and Machine Learning
1. Descriptive Statistics
2.Classical Inference Proportions
3. Classical InferenceMeans
4. Bayesian Analysis
5. Bayesian Inference Proportions
6. Bayesian Inference Means
7. Correlations
11. KNN
12. Decision Tree
13. Random Forests
14. OLS
15. Evaluating Linear Model
16. Ridge Regression
17. LASSO Regression
18. Interpolation
19. Perceptron Basic
20. Training Neural Network
21. Regression Neural Network
22. Clustering
23. Evaluating Cluster Model
24. kMeans
25. Hierarchal 26. Spectral
27. PCA
28. SVD
29. Low Dimensional
